A repo to summarize resources used in object pose estimation as well as viewpoint estimation.

In the following tables, 3D CAD model is noted as model and 2D pictured object is noted as object.

Contributions are welcome. Please see the Table of Content which lists the things included in this repo. If you wish to contribute within these boundaries, feel free to send a PR. If you have suggestions for new sections to be included, please raise an issue and discuss before sending a PR.

• Resources 😎
• Objects in the wild 📷
• Objects in the controlled environments 🎥
• 3D model datasets 🚲
• Rendering methods 🚴

In this table, Pix3D provides accurate 2D-3D alignment while others provide a coarse alignment.

PASCAL3D+ is the de facto benchmark used for viewpoint estimation.

Dataset	Sample image	Annotation	Statistics	Reference
Objectron		3D Bounding Box	15K annotated videos and 4M annotated images	CVPR 2021
ApolloCar3D		6D Pose + Mask	34 car models with 60K+ objects in 5,277 images	CVPR 2019
Pix3D		6D Pose + Mask	9 categories containing 395 models in 10,069 images	CVPR 2018
ObjectNet3D		Euler Angles + BoundingBox	100 categories with 201,888 objects in 90,127 images	ECCV 2016
PASCAL3D+		Euler Angles + BoundingBox	12 categories with 36,292 objects in 30,889 images	WACV 2014
KITTI		3D BoundingBox	80,256 objects in 14,999 images	CVPR 2012

This table lists the datasets commonly known as BOP: Benchmark 6D Object Pose Estimation, which provide accurate 3D object models and accurate 2D-3D alignment.

You can download all the BOP datasets here and use the toolkit provided by the organizers.

After downloading the data, you can use our code data/BOP/ply2obj.py to convert original .ply files to .obj files, and run data/BOP/create_annotation.py to create a single annotation file for all the scenes in a dataset.

Datasets format can be found here, we use instance id in our annotation to indicate different instances pictured in the same image.

Dataset	Sample image	Annotation	Statistics	Reference
GraspNet-1B		6D pose + Depth + Mask	88 models in 190 videos with 97,280 frames	CVPR 2020
NOCS		6D pose + Depth + NOCS	6 categories in 300K composited images and 8K real images	CVPR 2019
YCBInEOAT		6D pose + RGBD Video	5 models in 9 videos with 7449 frames with moving objects	IROS 2020
YCB-Video		6D Pose + Depth + Mask	21 models in 92 videos with 133,827 frames	RSS 2018
T-LESS		6D Pose + Depth	30 models in 20 videos with ~49K frames	WACV 2017
Doumanoglou		6D Pose + Depth	2 models in 3 videos with 183 frames	CVPR 2016
Tejani		6D Pose + Depth	6 models in 6 videos with 2,067 frames	ECCV 2014
Occluded-LINEMOD		6D Pose + Depth	8 models in 1,214 frames with 8,992 objects	ECCV 2014
LINEMOD		6D pose + Depth for one object	15 models in 15 videos with 18,273 frames	ACCV 2012

In order to testify the network generalization ability (tested on images containing unseen 3D models from the training set), the following dataset could be used to generate synthetic training data.

Notice that ABC contains generic and arbitrary industrial CAD models while ShapeNetCore and ModelNet contain common category objects such as cars and chairs.

• Neural 3D Mesh Renderer: Kato el al. CVPR 2018

• RenderNet: Thu et al. NIPS 2018

• BlenderProc

• blender-cli-rendering

• pvnet-rendering

PyBullet: a very popular one in the Robotics community.

• Glumpy: does not support headless rendering (failed on ssh mode)

• UnrealCV: extension of Unreal Engine 4, helps interact with virtual world and communicate with external program.

• SyntheticComputerVision: resuming a lot of techniques used to generate synthetic image