Blenderproc

## Overview

Blenderproc is a dataset for object detection tasks - it contains Conector Tapa annotations for 230 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Blender

## Overview

Blender is a dataset for object detection tasks - it contains Blender annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Blender Render Set

## Overview

Blender Render Set is a dataset for object detection tasks - it contains Aedes Aegypti annotations for 1,076 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

CORSMAL Hand-Occluded Containers (CHOC) is an image-based dataset for category-level 6D object pose and size estimation, affordance segmentation, object detection, object and arm segmentation, and hand+object reconstruction. The dataset has 138,240 pseudo-realistic composite RGB-D images of hand-held containers on top of 30 real backgrounds (mixed-reality set) and 3,951 RGB-D images selected from the CORSMAL Container Manipulation (CCM) dataset (real set). CHOC-AFF is the subset that focuses on the problem of visual affordance segmentation. CHOC-AFF consists of the RGB images, the object and arm segmentation masks, and the affordance segmentation masks. The images of the mixed-reality set are automatically rendered using Blender, and are split into 129,600 images of handheld containers and 8,640 images of objects without hand. Only one synthetic container is rendered for each image. Images are evenly split among 48 unique synthetic objects from three categories, namely 16 boxes, 16 drinking containers without stem (nonstems) and 16 drinking containers with stems (stems), selected from ShapeNetSem. For each object, 6 realistic grasps were manually annotated using GraspIt!: bottom grasp, natural grasp, and top grasp for the left and right hand. The mixed-reality set provides RGB images, depth images, segmentation masks (hand and object), normalised object coordinates images (only object), object meshes, annotated 6D object poses (orientation and translation in 3D with respect to the camera view), and grasp meshes with their MANO parameters. Each image has a resolution of 640x480 pixels. Background images were acquired using an Intel RealSense D435i depth camera, and include 15 indoor and 15 outdoor scenes. All information necessary to re-render the dataset is provided, namely backgrounds, camera intrinsic parameters, lighting, object models, and hand + forearm meshes, and poses; users can complement the existing data with additional annotations. Note: The mixed-reality set was built on top of previous works for the generation of synthetic and mixed-reality datasets, such as OBMan and NOCS-CAMERA. The images of the real set are selected from 180 representative sequences of the CCM dataset. Each image contains a person holding one of the 15 containers during a manipulation occurring in the video prior to a handover (e.g., picking up an empty container, shaking an empty or filled food box, or pouring a content into a cup or drinking glass). For each object instance, sequences were chosen under four randomly sampled conditions, including background and lighting conditions, scenarios (person sitting, with the object on the table; person sitting and already holding the object; person standing while holding the container and then walking towards the table), and filling amount and type. The same sequence is selected from the three fixed camera views (two side and one frontal view) of the CCM setup (60 sequences for each view). Fifteen sequences exhibit the case of the empty container for all fifteen objects, whereas the other sequences have the person filling the container with either pasta, rice or water at 50% or 90% of the full container capacity. The real set has RGB images, depth images and 6D pose annotations. For each sequence, the 6D poses of the containers are manually annotated every 10 frames if the container is visible in at least two views, resulting in a total of 3,951 annotations. Annotations of the 6D poses for the intermediate frames are also provided by using interpolation. Contacts For enquiries, questions, or comments, please contact Alessio Xompero. For enquiries, questions, or comments about CHOC-AFF, please contact Tommaso Apicella. References If you work on Visual Affordance Segmentation and you use the subset CHOC-AFF, please see the related work on ACANet and also cite: Affordance segmentation of hand-occluded containers from exocentric images T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023 Additional resources Webpage of 6D pose estimation using CHOC Toolkit to parse and inspect the dataset, or generate new data Release notes 2023/09/10 - Added object affordance segmentation masks 2023/02/08 - Fixed NOCS maps due to a missing rotation during the generation - Fixed annotations to include the missing rotation 2023/01/09 - Fixed RGB_070001_80000 (wrong files previously) 2022/12/14 - Added a mapping dictionary from grasp-IDs to their corresponding MANO-parameters-IDs to grasp.zip - Added object meshes with the NOCS textures/material in object_models.zip - Fixed folder name in annotations.zip - Updated README file to include these changes and fix a typo in the code block to unzip files

Blender_NEW 4

## Overview

Blender_NEW 4 is a dataset for object detection tasks - it contains Blender_NEW 4 annotations for 252 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Test Blender Synth Data

## Overview

Test Blender Synth Data is a dataset for object detection tasks - it contains Geometric Shape annotations for 329 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A dataset containing 3,346 synthetically generated RGB images of road segments with cracks. Road segments and crack formations created in Blender, data collected in Microsoft AirSim. Data is split into train (~70%), test (~15%), and validation (~15%) folders. Contains ground truth bounding boxes labelling cracks in both YOLO and COCO JSON format.

The dataset provided below has been synthetically created using Blender. A fundamental analysis on this data was conducted utilizing the YOLO V3 object detection technique to identify divots or areas of damage.

Used for paper:

Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection

https://github.com/stevefoy/Turfgrass-Divot-Object-Detection

@inproceedings{IMVIP2024,
  author = {Stephen Foy and Simon McLoughlin},
  title = {Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection},
  booktitle = {Irish Machine Vision and Image Processing Conference (IMVIP)},
  year = {2024}
}

Contents of the Zip File:

• synthDivot_416x416 Folder:

  • Train and validation subfolders
  • 1200 RGB PNG images
  • Corresponding masks for each image
  • Bounding box data in YOLO .txt format

• synthDivot_608x608 Folder:

  • Train and validation subfolders
  • 1200 RGB PNG images
  • Bounding box data in YOLO .txt format

Blender_2

## Overview

Blender_2 is a dataset for object detection tasks - it contains Objects annotations for 4,371 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

3749

## Overview

3749 is a dataset for object detection tasks - it contains Objects annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition

This repository contains the data synthesis pipeline and synthetic product recognition datasets proposed in [1].

Data Synthesis Pipeline:

We provide the Blender 3.1 project files and Python source code of our data synthesis pipeline pipeline.zip, accompanied by the FastCUT models used for synthetic-to-real domain translation models.zip. For the synthesis of new shelf images, a product assortment list and product images must be provided in the corresponding directories products/assortment/ and products/img/. The pipeline expects product images to follow the naming convention c.png, with c corresponding to a GTIN or generic class label (e.g., 9120050882171.png). The assortment list, assortment.csv, is expected to use the sample format [c, w, d, h], with c being the class label and w, d, and h being the packaging dimensions of the given product in mm (e.g., [4004218143128, 140, 70, 160]). The assortment list to use and the number of images to generate can be specified in generateImages.py (see comments). The rendering process is initiated by either executing load.py from within Blender or within a command-line terminal as a background process.

Datasets:

• SG3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 851,801 instances of 3,234 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SG3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SGI3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 838,696 instances of 1,063 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SGI3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SPS8k - Synthetic Product Shelves 8k (SPS8k) dataset, comprised of 16,224 synthetic shelf images with 1,981,967 instances of 8,112 supermarket products. Instance-level bounding boxes and GTIN class labels are provided for all product instances.
• SPS8kt - Domain-translated version of SPS8k, utilizing SKU110k as the target domain. Instance-level bounding boxes and GTIN class labels for all product instances.

Table 1: Dataset characteristics.

Sample Format

A sample consists of an RGB image (i.png) and an accompanying label file (i.txt), which contains the labels for all product instances present in the image. Labels use the YOLO format [c, x, y, w, h].

¹SG3k and SG3kt use generic pseudo-GTIN class labels, created by combining the GroZi-3.2k food product category number i (1-27) with the product image index j (j.jpg), following the convention i0000j (e.g., 13000097).

²SGI3k and SGI3kt use the generic GroZi-3.2k class labels from https://arxiv.org/abs/2003.06800.

Download and Use
This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, Julian, and Martin Kampel. "Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition." International Conference on Computer Analysis of Images and Patterns. Cham: Springer Nature Switzerland, 2023.

BibTeX citation:

@inproceedings{strohmayer2023domain,
 title={Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition},
 author={Strohmayer, Julian and Kampel, Martin},
 booktitle={International Conference on Computer Analysis of Images and Patterns},
 pages={239--250},
 year={2023},
 organization={Springer}
}

3d_Blender_All_Resolution

## Overview

3d_Blender_All_Resolution is a dataset for object detection tasks - it contains Package annotations for 4,328 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Blender_chairs_p1

## Overview

Blender_chairs_p1 is a dataset for object detection tasks - it contains Chairs annotations for 1,734 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Blender_gauge_hdri_connector

## Overview

Blender_gauge_hdri_connector is a dataset for object detection tasks - it contains Gauge annotations for 500 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).