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Resources (papers, datasets, rendering methods) in the domain of object pose estimation.

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Awesome Object Pose Estimation [Paper List]

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.

Contributing

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.

Table of Content

Objects in the wild 📷

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

Objects in the controlled environments 🎥

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

3D model datasets 🚲

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.

Dataset Categories Models in total Reference
ABC - 1 million CVPR 2019
ShapeNetCore 55 ~51,300 ArXiv 2015
ModelNet-40 40 12,311 CVPR 2015

Rendering methods 🚵

Differentiable Renderer

Blender Render :

Physical Simulator

PyBullet: a very popular one in the Robotics community.

Others

  • 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

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