yuchen0187/partnet-mobility dataset hosted on Hugging Face and contributed by the HF Datasets community

luyu1021/partnet-mobility-multiview dataset hosted on Hugging Face and contributed by the HF Datasets community

This is the GAPartNet dataset.

Currently, objects from PartNet Mobility have already been released. If you want to download this dataset, please complete the following form. If you think this dataset useful, please consider cite this work: @article{geng2022gapartnet,  title={GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts},  author={Geng, Haoran and Xu, Helin and Zhao, Chengyang and Xu, Chao and Yi, Li and… See the full description on the dataset page: https://huggingface.co/datasets/yangyandan/GAPartNet_PhyScene.

Partner SLA Monitoring for Mobility Apps Market Outlook

According to our latest research, the global Partner SLA Monitoring for Mobility Apps market size reached USD 1.56 billion in 2024, reflecting robust demand driven by the digital transformation of mobility services worldwide. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 4.23 billion by 2033. This growth is underpinned by the increasing need for real-time service performance tracking, stringent compliance requirements, and the proliferation of multi-partner mobility ecosystems. As per our latest research, the adoption of advanced analytics and automation tools is further accelerating the expansion of the Partner SLA Monitoring for Mobility Apps market.

One of the primary growth factors for the Partner SLA Monitoring for Mobility Apps market is the rapid evolution of the mobility sector, characterized by the emergence of diverse service models such as ride-hailing, car-sharing, micro-mobility, and integrated public transit platforms. As mobility services become more interconnected, the complexity of managing and monitoring Service Level Agreements (SLAs) across multiple partners has increased significantly. Mobility service providers are increasingly investing in robust SLA monitoring solutions to ensure seamless user experiences, minimize downtime, and maintain competitive differentiation. The demand for accurate, real-time performance metrics and automated alerting mechanisms has become crucial for managing the intricate web of third-party relationships that define modern mobility ecosystems.

Another significant driver propelling the Partner SLA Monitoring for Mobility Apps market is the growing regulatory scrutiny and customer expectations regarding service reliability and transparency. Governments and regulatory bodies across key regions are introducing stricter compliance mandates for mobility operators, particularly concerning uptime, data privacy, and passenger safety. These regulations necessitate comprehensive SLA monitoring frameworks that can deliver auditable reports and ensure contractual obligations are consistently met. Additionally, end-users are increasingly demanding transparency in service quality, further pushing mobility companies to adopt sophisticated monitoring tools that can proactively identify and resolve issues before they impact the customer experience.

Technological advancements are also playing a pivotal role in shaping the Partner SLA Monitoring for Mobility Apps market. The integration of artificial intelligence, machine learning, and advanced analytics into SLA monitoring platforms is enabling predictive insights, automated incident management, and enhanced partner collaboration. These technologies empower mobility service providers and fleet operators to not only monitor compliance in real time but also to anticipate potential breaches and optimize partner performance proactively. The shift towards cloud-based solutions and scalable SaaS models is making it easier for organizations of all sizes to deploy and manage comprehensive SLA monitoring systems, further fueling market growth.

From a regional perspective, North America currently leads the Partner SLA Monitoring for Mobility Apps market, driven by the presence of major mobility service providers, advanced IT infrastructure, and a high level of regulatory oversight. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, supported by rapid urbanization, increasing smartphone penetration, and the expansion of smart mobility initiatives in countries like China, India, and Japan. Europe also holds a significant market share, benefiting from its established public transit networks and progressive mobility regulations. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, albeit at a slower pace, due to ongoing investments in urban mobility solutions and digital transformation efforts.

Component Analysis

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Dataset Card for Dataset Name

Preprocessed dataset of PartNet-Mobility objects for FreeArt3D.

  Dataset Sources

Repository:: https://github.com/CzzzzH/FreeArt3D Paper: https://huggingface.co/papers/2510.25765 Demo: https://huggingface.co/spaces/MorPhLingXD/FreeArt3D

BibTeX: @InProceedings{chen2025freeart3d, title = {FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion}, author = {Chen, Chuhao and Liu, Isabella and Wei, Xinyue and Su, Hao and… See the full description on the dataset page: https://huggingface.co/datasets/MorPhLingXD/FreeArt3D.

Change of place of residence and motives for this, choice of place of residence and housing conditions in connection with change of work, marriage or social activities. Topics: Time of occurrence of desire for change of place of residence; length of residence at previous place of residence; persons involved in change of place of residence; marital status at time of change of place of residence; connection between change of place of residence and place of work; work at place of residence; change of place of residence or workplace since conclusion of vocational training; motives for change of place of residence in personal and occupational area (scale); connection with wedding; number of residents at former and current place of residence; size and type of former and current residential building; participation in social activities at former and current place of residence (scale); satisfaction with living conditions at former and current place of residence (scale); reasons for change of place of residence (scale); current desire for change of place of residence; length of employment; current work in occupation learned; length of possession of a personal residence; acquisition of residence at change of place of residence; degree of adaptation to new place of residence; marital status; number of children; current and future highest occupational qualification of respondent; highest occupational qualification of partner, father, mother; occupation of respondent, partner, mother, father; work area of respondent, partner.

This repository contains the synthetic data used in the paper iTACO: Interactable Digital Twins of Articulated Objects from Casually Captured RGBD Videos

  Term of Use

Our dataset is derived from the PartNet-Mobility dataset. Users are required to agree on the terms of use of the PartNet-Mobility dataset before using our dataset. Researchers shall use our dataset only for non-commercial research and educational purposes.

  File Structure

Inside the sim_data folder, there are… See the full description on the dataset page: https://huggingface.co/datasets/3dlg-hcvc/video2articulation.

Dataset: Articulat3D-Sim

Official dataset repository for the paper "Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints". This dataset comprising 17 object categories from PartNet-Mobility: Camera, Chair, Coffee Machine, Dispenser, Door, Eyeglasses, Lamp, Lighter, Oven, Printer, Refrigerator, Safe, Stapler, Storage Furniture, Switch, Table, and Toilet. This dataset consists of 24 sequences featuring complex kinematics… See the full description on the dataset page: https://huggingface.co/datasets/ShawnRicardo/Articulat3D-Sim.

This repo contains the data for S2O: Static to Openable Enhancement for Articulated 3D Objects. See the code on GitHub and the paper for details. Please cite S2O [1] if you use ACD. We provide the mesh, point cloud, and metadata for the two datasets used in S2O.

PM-Openable - This is a subset of 648 openable objects from full PartNet-Mobility [2]. We use a train/val/test split of 460/95/93 objects.
Articulated Container Dataset (ACD) [1] - We take openable container objects from HSSD [3]… See the full description on the dataset page: https://huggingface.co/datasets/3dlg-hcvc/s2o.