The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:

• Point cloud 1: (x,y,z, (Bool)Is_Object): In this point cloud, the best performance of the Deep Learning model is expected as ground truth information is provided as the additional feature of each point.
  • Point cloud 1A: (x,y,z, (Bool)Is_Car): the additional feature of each point that belongs to an object of the ’Car’ type has a Boolean 1.0 value; contrariwise, the 0.0 value was used. File: velodyne_1A_isCar;
  • Point cloud 1B: (x,y,z, (Bool)Is_Ped): the additional feature of each point that belongs to an object of the ’Pedestrian’ type has a Boolean 1.0 value; contrariwise, the value 0.0 was used. File: velodyne_1A_isPed.
• Point cloud 2: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_2_radial_velocity;
• Point cloud 3: (x,y,z,(Float)Car_Absolute_Speed): in this point cloud, every point of a ’Car’ type object
  has the absolute speed of the object as the additional feature. File: velodyne_3_car_abs_speed;
• Point cloud 4: (x,y,z,(Bool)Car_Is_Moving): the additional feature of a ’Car’ type object is a Boolean value that is set to 1.0 if the vehicle is moving, contrariwise is set to 0.0 for other object categories or if the vehicle is static. File: velodyne_4_car_is_moving;
• Point cloud 5: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_5_xyz;

Additionally, the label split for testing and training sets used can be found at file: Labels_split.

This work was made as part of a master thesis. For further details, please check the dataset generation source code [1]. Any further questions please contact Leandro Alexandrino (l.alexandrino@ua.pt).

[1] - Fork deepgtav-presil - leandro alexandrino, https://github.com/leandroalexandrino1995/DeepGTAVPreSIL.

LiDAR-CS is a dataset for 3D object detection in real traffic. It contains 84,000 point cloud frames under 6 groups of different sensors but with same corresponding scenarios, captured from hybrid realistic LivDAR simulator.

The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the training and testing sets, that include KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:

• Point cloud 1: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_radial_velocity;
• Point cloud 2: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_abs_speed;
• Point cloud 3: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_is_moving;
• Point cloud 4: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_xyz;

Additionally, the detections generated with the OpenPCDet toolbox and Second-IoU model are provided.

This work was made as part of a master thesis of Informatics Engineering in the University of Aveiro.

LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measurement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1400 objects and classified it into 6 groups of object namely, human, cars, motorcyclist, signboard, road divider and others.

The Lidar Object Processing Software market is experiencing robust growth, driven by the increasing adoption of LiDAR technology across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the automotive industry's push towards autonomous vehicles significantly boosts demand for sophisticated software capable of processing vast amounts of LiDAR data for precise object detection and classification. Secondly, the burgeoning robotics sector relies heavily on accurate object recognition for navigation and manipulation, further driving market growth. Advancements in LiDAR sensor technology, producing higher resolution and more detailed point cloud data, necessitates more powerful and efficient processing software. Finally, the expansion of applications in aerospace, electric power infrastructure inspection, and mapping & surveying contributes to market expansion. While the exact market size in 2025 is unavailable, given a typical CAGR (let's assume 15% based on industry trends) for such high-growth markets and a conservative starting point, we can estimate a market size of around $350 million in 2025. The market is segmented by application (unmanned, robot, aerospace, electric power, others) and type (voxel-based, point-based, point-voxel), allowing for tailored solutions and specialization. The competitive landscape features established players like Hexagon and Velodyne Lidar, along with emerging technology providers like Mapix Technologies and Blickfeld. Geographic expansion is prominent, with North America and Europe currently holding significant market shares, followed by a rapidly developing Asia-Pacific region. The forecast period (2025-2033) promises continued strong growth, propelled by ongoing technological advancements, increasing automation across various industries, and the development of novel applications for LiDAR data processing. Key challenges include the need for robust algorithms to handle complex environments, the integration of LiDAR data with other sensor modalities, and the development of software solutions that are both powerful and computationally efficient. The market will continue to be shaped by the ongoing evolution of LiDAR technology itself, with higher point density, longer range, and improved accuracy driving further demand for advanced software solutions. Continued investment in research and development and strategic partnerships will be crucial for companies to maintain a competitive edge.

ONCE (One millioN sCenEs) is a dataset for 3D object detection in the autonomous driving scenario. The ONCE dataset consists of 1 million LiDAR scenes and 7 million corresponding camera images. The data is selected from 144 driving hours, which is 20x longer than other 3D autonomous driving datasets available like nuScenes and Waymo, and it is collected across a range of different areas, periods and weather conditions.

Consists of:

1 Million LiDAR frames, 7 Million camera images

200 km² driving regions, 144 driving hours

15k fully annotated scenes with 5 classes (Car, Bus, Truck, Pedestrian, Cyclist)

Diverse environments (day/night, sunny/rainy, urban/suburban areas)

UA_L-DoTT (University of Alabama’s Large Dataset of Trains and Trucks) is a collection of camera images and 3D LiDAR point cloud scans from five different data sites. Four of the data sites targeted trains on railways and the last targeted trucks on a four-lane highway. Low light conditions were present at one of the data sites showcasing unique differences between individual sensor data. The final data site utilized a mobile platform which created a large variety of view points in images and point clouds. The dataset consists of 93,397 raw images, 11,415 corresponding labeled text files, 354,334 raw point clouds, 77,860 corresponding labeled point clouds, and 33 timestamp files. These timestamps correlate images to point cloud scans via POSIX time. The data was collected with a sensor suite consisting of five different LiDAR sensors and a camera. This provides various viewpoints and features of the same targets due to the variance in operational characteristics of the sensors. The inclusion of both raw and labeled data allows users to get started immediately with the labeled subset, or label additional raw data as needed. This large dataset is beneficial to any researcher interested in machine learning using cameras, LiDARs, or both.

The full dataset is too large (~1 Tb) to be uploaded to Mendeley Data. Please see the attached link for access to the full dataset.

3D LiDAR Sensor Market Outlook

The 3D LiDAR sensor market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 4.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 13.5% during the forecast period. The rapid growth of the market can be attributed to the increasing demand for advanced sensing technologies in various sectors, including automotive, aerospace, and industrial applications. The adoption of autonomous vehicles and advanced driver-assistance systems (ADAS), which rely heavily on LiDAR technology for real-time 3D mapping and obstacle detection, is a significant growth factor driving the market.

The growing emphasis on safety and efficiency in transportation has spurred the development and deployment of LiDAR systems in autonomous vehicles. These sensors provide precise and accurate distance measurements, enabling better navigation and object detection. Additionally, the ongoing advancements in technology, such as the development of solid-state LiDAR, have made these sensors more compact, reliable, and cost-effective, further fueling their adoption across various industries. Furthermore, the integration of LiDAR technology with other sensing and imaging technologies, such as cameras and radar, is creating new opportunities for enhanced performance and functionality, thereby driving market growth.

Another significant growth factor for the 3D LiDAR sensor market is the increasing use of LiDAR in mapping and surveying applications. LiDAR technology offers high-resolution and accurate data, making it ideal for creating detailed topographic maps and 3D models of various terrains and structures. This capability is particularly beneficial for urban planning, environmental monitoring, and disaster management. The growing awareness of the benefits of LiDAR technology, coupled with the increasing availability of affordable LiDAR systems, is driving the adoption of these sensors in the mapping and surveying sector.

Moreover, the rising demand for automation and robotics in industrial applications is contributing to the growth of the 3D LiDAR sensor market. LiDAR sensors are used in various industrial applications, such as material handling, warehouse automation, and machine vision, to enhance precision and efficiency. The ability of LiDAR to provide real-time 3D data and improve situational awareness makes it an essential component in the automation and robotics industry. As industries continue to invest in automation technologies to improve productivity and reduce operational costs, the demand for LiDAR sensors is expected to grow significantly.

Solid State MEMS LiDAR represents a significant advancement in the field of LiDAR technology, offering a compact and efficient solution for various applications. Unlike traditional mechanical LiDAR systems, Solid State MEMS LiDAR utilizes microelectromechanical systems (MEMS) to steer the laser beams, eliminating the need for moving parts. This innovation not only reduces the size and weight of the LiDAR units but also enhances their durability and reliability. The integration of MEMS technology allows for faster scanning speeds and improved resolution, making it ideal for applications in autonomous vehicles, drones, and portable devices. As industries continue to seek more efficient and cost-effective sensing solutions, Solid State MEMS LiDAR is poised to play a crucial role in driving the adoption of LiDAR technology across diverse sectors.

Regionally, North America is anticipated to hold the largest share of the 3D LiDAR sensor market during the forecast period, driven by the presence of key industry players and the high adoption of advanced technologies in the region. The Asia Pacific region is expected to witness the highest growth rate, owing to the rapid industrialization, increasing investments in autonomous vehicles, and rising demand for advanced mapping and surveying solutions.

Product Type Analysis

The 3D LiDAR sensor market is segmented into two main product types: Mechanical LiDAR and Solid-State LiDAR. Mechanical LiDAR systems have been traditionally used due to their high accuracy and reliability. These systems use rotating mirrors or prisms to emit laser pulses and capture the reflected signals, providing detailed 3D images of the surroundings. Mechanical LiDAR has been widely adopted in various applications, including autonomous vehicles and industrial automation, due to its ability to provide high-resolution and accurate

The automotive grade 3D LiDAR sensor market is experiencing explosive growth, driven by the accelerating adoption of Advanced Driver-Assistance Systems (ADAS) and the burgeoning development of autonomous vehicles. The market, currently valued at approximately $2 billion in 2025, is projected to exhibit a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the increasing demand for enhanced safety features in vehicles is pushing automakers to integrate sophisticated sensor technologies like 3D LiDAR, which provides superior object detection and range capabilities compared to traditional cameras and radar. Secondly, the continuous advancements in LiDAR technology, particularly the emergence of more cost-effective solid-state LiDAR, are making this technology more accessible for mass-market vehicle integration. Finally, supportive government regulations and increasing investments in autonomous vehicle research and development are further accelerating market growth. Competition is intense, with established automotive suppliers like Valeo and Continental vying for market share alongside innovative LiDAR specialists such as Luminar, Velodyne, and Hesai Tech. While the high initial cost of LiDAR remains a restraint, ongoing technological advancements and economies of scale are expected to mitigate this challenge over time. Segmentation within the market reveals a strong bias towards the ADAS application segment, currently dominating market share. However, the self-driving segment is poised for rapid growth in the coming years, representing a significant future market opportunity. In terms of LiDAR types, solid-state LiDAR is gaining traction due to its inherent advantages in reliability, cost-effectiveness, and robustness. While mechanical LiDAR currently holds a larger market share, the ongoing transition towards solid-state technology is expected to reshape the market landscape over the forecast period. Geographically, North America and Europe are currently the leading markets, driven by high vehicle ownership rates, robust automotive industries, and advanced technological infrastructure. However, the Asia Pacific region, particularly China, is anticipated to witness significant growth, driven by substantial investments in autonomous driving technologies and the expansion of the domestic automotive industry. The market's trajectory suggests a future dominated by highly sophisticated, cost-effective 3D LiDAR solutions integrated across the automotive landscape, fundamentally reshaping driving safety and mobility.

The global 360° Laser Scanning Ranging Radar market is experiencing robust growth, driven by the increasing demand for advanced sensor technologies across diverse sectors. The automotive industry, particularly in the development of autonomous vehicles, is a key driver, demanding high-precision and reliable ranging capabilities for object detection and navigation. The proliferation of intelligent mobile robots and unmanned aerial vehicles (UAVs) further fuels market expansion, as these applications require sophisticated perception systems for safe and efficient operation. The market is segmented by application (Intelligent Mobile Robots, UAVs, Automotive, Others) and by type (2D Laser, 3D Laser), with 3D laser technology gaining significant traction due to its superior spatial resolution and object recognition capabilities. We estimate the 2025 market size to be approximately $2.5 billion, based on observed growth in related sensor markets and technological advancements. A compound annual growth rate (CAGR) of 15% is projected for the forecast period (2025-2033), indicating substantial market expansion. While challenges such as high initial costs and technological complexities remain, ongoing innovation and decreasing manufacturing costs are expected to mitigate these restraints and drive further market penetration. The competitive landscape is characterized by a mix of established players and emerging companies. Key players like Velodyne Lidar and others are investing heavily in research and development to enhance performance, reduce costs, and broaden application reach. Regional growth is expected to be geographically diverse, with North America and Asia Pacific leading the market, fueled by strong technological advancements and significant investments in autonomous driving initiatives. Europe is also witnessing significant growth, driven by government regulations and initiatives promoting the development and adoption of advanced driver-assistance systems (ADAS) and autonomous vehicles. The market's future growth hinges on continued advancements in sensor technology, decreasing production costs, wider industry adoption, and the development of robust safety standards for autonomous systems. Furthermore, the integration of 360° Laser Scanning Ranging Radars with other sensor technologies, such as cameras and LiDAR, to create comprehensive perception systems will be a critical trend shaping the market's trajectory.

2D 3D Lidar Sensors Market Outlook

The global 2D and 3D Lidar sensors market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach around USD 5.9 billion by 2032, growing at an impressive compound annual growth rate (CAGR) of 14.2% during the forecast period. This substantial growth is driven by increasing adoption in various applications such as autonomous vehicles, industrial automation, and advanced mapping and surveying technologies.

One of the primary growth factors for the 2D and 3D Lidar sensors market is the booming automotive industry, especially the autonomous vehicle segment. Lidar sensors are critical for the navigation and safety systems in self-driving cars, offering precise distance measurement and obstacle detection capabilities. As automakers and tech companies invest heavily in autonomous driving technology, the demand for advanced Lidar sensors is expected to skyrocket. Additionally, government regulations and safety standards mandating the use of advanced driver-assistance systems (ADAS) further bolster the market growth.

Another significant driver is the rising need for industrial automation and robotics. In manufacturing and logistics, the use of Lidar sensors enhances efficiency and safety by enabling precise navigation and object detection. The trend towards smart factories and Industry 4.0 initiatives, which emphasize automation and data exchange in manufacturing technologies, is expected to fuel the adoption of 2D and 3D Lidar sensors. These sensors are indispensable for applications such as automated guided vehicles (AGVs) and robotic arms, ensuring accurate and reliable performance in complex industrial environments.

Technological advancements are also playing a crucial role in the growth of the 2D and 3D Lidar sensors market. Innovations in Lidar technology, such as the development of solid-state Lidar and improvements in sensor resolution and range, are expanding the potential applications of these sensors. As Lidar systems become more compact, cost-effective, and efficient, their integration into a broader range of devices and systems becomes feasible. This trend is expected to open new avenues for market growth across various sectors.

From a regional perspective, North America and Europe are currently leading the market, driven by significant investments in autonomous vehicles and industrial automation. However, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period, fueled by the rapid industrialization, urbanization, and growing adoption of advanced technologies in countries like China, Japan, and South Korea. This regional growth trend reflects the global shift towards embracing innovative technologies to enhance productivity and safety across industries.

Product Type Analysis

The 2D and 3D Lidar sensors market is segmented into 2D Lidar sensors and 3D Lidar sensors. The 2D Lidar sensors segment is primarily used in applications where depth information is not critical. These sensors are widely used in industrial automation, particularly in areas such as material handling and warehouse management. Their ability to provide accurate distance measurement and object detection in a single plane makes them highly suitable for such applications. Despite being less advanced than 3D Lidar sensors, 2D Lidar sensors offer a cost-effective solution for many industrial uses.

On the other hand, 3D Lidar sensors are gaining significant traction due to their ability to provide detailed three-dimensional information about the surrounding environment. This capability is crucial for applications requiring high precision and reliability, such as autonomous driving and advanced robotics. 3D Lidar sensors are more complex and expensive than their 2D counterparts, but their benefits in terms of accuracy and range make them indispensable for cutting-edge technologies. As the costs of 3D Lidar sensors continue to decrease, their adoption is expected to increase across various sectors.

In the automotive sector, 3D Lidar sensors are essential for the development of autonomous vehicles, where they are used for environment mapping, obstacle detection, and navigation. The high-resolution data provided by 3D Lidar sensors enables self-driving cars to navigate complex environments safely and efficiently. Similarly, in the aerospace and defense sector, these sensors are used for applications such as drone navigation and surveillance, where accurate 3D mapping is critical.

The growing need for advanced mapping an

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.

The global long-range 3D LiDAR market is experiencing robust growth, driven by the increasing demand for autonomous vehicles, advanced robotics, and industrial automation. This market is projected to reach a significant size, expanding at a considerable Compound Annual Growth Rate (CAGR). While precise figures for market size and CAGR aren't provided, based on industry reports and the rapid technological advancements in LiDAR technology, a reasonable estimation would place the 2025 market value at approximately $2 billion, with a CAGR exceeding 20% from 2025 to 2033. This growth is fueled by several factors, including the continuous improvement in LiDAR sensor performance (longer range, higher accuracy, and improved cost-effectiveness), the rising adoption of autonomous driving systems in various sectors, and the increasing need for precise 3D mapping and object detection in industrial settings. The market is segmented by LiDAR type (mechanical and solid-state) and application (autonomous driving, robotics, industrial automation, and others). Solid-state LiDAR is gaining traction due to its advantages in terms of robustness, size, and cost, gradually displacing mechanical LiDAR in certain applications. The autonomous driving segment holds a substantial share of the market, but growth is also expected from the robotics and industrial automation segments, particularly in areas such as warehouse automation and smart manufacturing. Geographical expansion is another key driver, with North America and Asia-Pacific expected to be significant contributors to the overall market growth. However, the market faces certain challenges. High initial costs associated with LiDAR technology and the complexity of integrating LiDAR systems into diverse applications remain significant restraints. Competition among numerous players in the market, including established automotive suppliers and emerging LiDAR technology specialists, further influences market dynamics. Ongoing technological innovations and the development of more cost-effective and efficient LiDAR systems are crucial for broader market adoption and sustained growth in the coming years. The success of the long-range 3D LiDAR market is intrinsically tied to the progress and wider deployment of autonomous and robotic systems across various industries.

The global 2D and 3D LiDAR sensor market is experiencing robust growth, driven by the increasing adoption of autonomous vehicles, advanced driver-assistance systems (ADAS), and the expansion of robotics across various sectors. The market is segmented by sensor type (2D and 3D) and application (automotive, industrial manufacturing, service robotics, and others). While 3D LiDAR sensors currently command a larger market share due to their superior perception capabilities, particularly in autonomous driving applications, 2D LiDAR sensors maintain a significant presence in simpler applications like automated guided vehicles (AGVs) and security systems. The high CAGR indicates a sustained period of growth, fueled by ongoing technological advancements leading to improved sensor accuracy, range, and affordability. Key restraints include high initial investment costs and the need for robust data processing infrastructure to effectively utilize LiDAR data. However, these challenges are being mitigated through continuous innovation and economies of scale. The automotive and industrial manufacturing sectors are the primary growth drivers, with significant potential for expansion in service robotics and other emerging applications like smart cities and agriculture. The North American and Asian markets are currently leading the adoption of LiDAR technology, but growth is expected across all regions as technological maturity and cost reductions facilitate wider adoption. The forecast period (2025-2033) anticipates significant market expansion, with 3D LiDAR sensors projected to witness faster growth compared to 2D sensors. This is due to increasing demand for high-resolution 3D mapping and object detection capabilities in autonomous vehicles and advanced robotics. The market is characterized by intense competition among established players like Valeo, SICK AG, and Velodyne Lidar, as well as emerging innovative companies from China and other regions. Strategic partnerships, mergers and acquisitions, and continuous product innovation are expected to shape the market landscape in the coming years. Market penetration in developing economies holds substantial potential, especially in regions with rapidly growing infrastructure development and industrial automation initiatives. The evolution of LiDAR technology towards smaller, lighter, and more energy-efficient sensors will further accelerate market adoption across various application areas.

The global vehicle grade 3D LiDAR sensor market is experiencing explosive growth, projected to reach a value of $454.8 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 47.4% from 2025 to 2033. This surge is primarily driven by the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and the accelerating development of autonomous driving technologies in the automotive industry. The demand for enhanced safety features, precise object detection and mapping capabilities, and improved vehicle navigation are key factors fueling this market expansion. Solid-state LiDAR technology is gaining significant traction due to its advantages in terms of cost-effectiveness, reliability, and size compared to mechanical LiDAR systems. However, challenges such as high initial costs, limited range in some solid-state solutions, and the need for robust regulatory frameworks for autonomous vehicles represent potential restraints on market growth. The market is segmented by LiDAR type (solid-state and mechanical) and application (ADAS and self-driving), with the self-driving segment anticipated to witness the most significant growth in the coming years due to its higher reliance on accurate and extensive sensor data. Geographic distribution reveals robust growth across North America, Europe, and Asia-Pacific, with China and the United States emerging as leading markets due to substantial investments in autonomous vehicle development and supportive government policies. The competitive landscape is characterized by a mix of established automotive component suppliers like Valeo and Continental, and innovative technology companies like Hesai Tech, Luminar, and Velodyne. This competitive intensity is driving innovation, leading to improvements in sensor performance, cost reduction, and wider deployment of LiDAR technology across various vehicle segments. Ongoing research and development efforts focused on improving LiDAR sensor accuracy, range, and environmental resilience are further expected to propel market expansion. The integration of LiDAR with other sensor technologies, such as cameras and radar, to create comprehensive perception systems for vehicles is a significant emerging trend, enhancing the overall safety and functionality of autonomous driving systems. The coming years will see a continuous refinement in LiDAR technology, paving the way for increased accessibility and wider adoption across the automotive sector.

The European LiDAR market, currently experiencing robust growth, is projected to maintain a significant Compound Annual Growth Rate (CAGR) of 23.60% from 2025 to 2033. This expansion is fueled by increasing adoption across diverse sectors. The automotive industry's drive towards autonomous driving necessitates high-precision mapping and object detection, significantly boosting LiDAR demand. Simultaneously, the engineering and surveying sectors leverage LiDAR for detailed 3D modeling in infrastructure development and construction projects. Furthermore, advancements in sensor technology, including miniaturization and improved accuracy, are lowering costs and broadening LiDAR's applications. Ground-based LiDAR systems, owing to their versatility and cost-effectiveness in specific applications, currently hold a larger market share compared to aerial LiDAR. However, the increasing affordability and sophistication of aerial LiDAR are expected to fuel its growth, particularly in large-scale mapping projects. Competition amongst established players like Leica Geosystems, Topcon, and Trimble, alongside emerging innovators, fosters innovation and drives down prices, benefiting end-users. While data privacy concerns and regulatory hurdles represent potential restraints, the overall market outlook remains overwhelmingly positive, driven by technological advancements and increasing demand across key industries. Geographic distribution within Europe shows strong growth potential across key economies like the United Kingdom, Germany, and France, which are early adopters of advanced technologies and have robust infrastructure development programs. These nations are expected to contribute significantly to the overall market value. However, other European countries are also witnessing increasing adoption, particularly in sectors like agriculture and environmental monitoring. Therefore, consistent growth is anticipated across the European region throughout the forecast period. The segment breakdown within Europe largely mirrors the global trends, with a focus on engineering, automotive, and industrial applications, and a growing contribution from aerospace and defense sectors. Continued investment in R&D, coupled with a focus on integrating LiDAR technology with other sensor systems and artificial intelligence, will drive further market expansion. Recent developments include: June 2022 - Stellantis has selected Valeo's third-generation LiDAR to equip multiple models of its different automotive brands from 2024. The Valeo SCALA 3 LiDAR will enable these vehicles to be certified for level 3 automation. Valeo's third-generation LiDAR sees everything, even if it is far ahead and invisible to the human eye. It can detect objects more than 150 meters away that the human eye, cameras, and radars cannot, such as small objects with very low reflectivity., January 2022 - Blickfeld, a Munich-based startup, has launched Qb2, a smart LiDAR. The Qb2 is an integrated smart LiDAR that combines software and hardware in a single device and does not require additional computers, servers, or adaptor boxes to provide efficient 3D data capture and processing in a single unit. Blickfeld's patented and industry-proven MEMS (microelectromechanical systems) LiDAR hardware technology is combined with a powerful compute module in the smart device.. Key drivers for this market are: Fast Paced Developments and Increasing Applications of Drones, Increasing Adoption in the Automotive Industry. Potential restraints include: Fast Paced Developments and Increasing Applications of Drones, Increasing Adoption in the Automotive Industry. Notable trends are: Engineering​ Industry to Hold Considerable Market Share.

An important factor in advancing autonomous driving systems is simulation. Yet, there is rather small progress for transferability between the virtual and real world. We revisit this problem for 3D object detection on LiDAR point clouds and propose a dataset generation pipeline based on the CARLA simulator. Utilizing domain randomization strategies and careful modeling, we are able to train an object detector on the synthetic data and demonstrate strong generalization capabilities to the KITTI dataset.

The dataset contains point clouds and bounding box labels for training 3D object detection.

The Industrial LiDAR market is experiencing robust growth, driven by increasing automation across various sectors and advancements in sensor technology. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The automotive industry's burgeoning adoption of autonomous driving systems is a major catalyst, demanding high-precision LiDAR sensors for object detection and navigation. Similarly, the rail transit sector is leveraging LiDAR for infrastructure monitoring, enhancing safety and operational efficiency. Further expansion is expected from the increasing integration of LiDAR in robotics, 3D mapping, and industrial automation processes. The diverse applications across these sectors contribute to the market's dynamism. Different wavelength LiDARs (905nm, 1550nm, 1064nm, 885nm) cater to specific needs, with 905nm currently dominating due to its cost-effectiveness and performance in short-range applications. However, the longer wavelengths (1550nm) are gaining traction due to their superior performance in challenging environmental conditions like fog or rain. Several trends are shaping the future of this market. The miniaturization and cost reduction of LiDAR sensors are expanding accessibility across various applications. Furthermore, the development of more robust and sophisticated algorithms for data processing enhances the accuracy and reliability of LiDAR systems. While the market faces certain restraints such as the high initial investment costs associated with LiDAR technology and potential regulatory hurdles, the overall growth trajectory remains overwhelmingly positive, indicating a promising outlook for the foreseeable future. The competitive landscape is populated by a mix of established players like Trimble, Hexagon, and Sick AG, as well as innovative startups, fostering innovation and driving further market expansion. Regional growth will likely be strongest in North America and Asia-Pacific, reflecting high technological adoption rates and significant investments in these regions' industrial sectors. This comprehensive report provides an in-depth analysis of the rapidly expanding Industrial LiDAR market, projected to reach $4 billion by 2028. It offers a granular view of market segmentation, key players, emerging trends, and future growth prospects, empowering stakeholders to make informed strategic decisions. The report leverages rigorous market research and data analysis to deliver actionable insights into this transformative technology.

UCF UrbanTwin (UT) dataverse is a collection of high-fidelity synthetic roadside lidar datasets. Each dataset in this dataverse supports core perception tasks in intelligent transportation systems, including 3D object detection, multi-object tracking, semantic segmentation, and instance segmentation. Generated through precise simulation of real-world intersections and sensor settings, these datasets exhibit minimal sim-to-real domain gap and enable the training of deep learning models that perform competitively on real data benchmarks. This synthetic data replicates subset R2 sequence 03 of the original TUMTraf-I dataset, containing labeled intersection lidar data. It is designed to mirror the physical layout of the target location, Garching bei München intersection in Germany. The sensors are also modeled after the sensor specifications of the original real dataset. The dataset contains ~50K points and 9 road-user objects per frame in an 80x80x10 meter^3 region roughly centered at the intersection area, all matching closely to the original dataset.