# Category-level Object Detection, Pose Estimation and Reconstruction from Stereo Images

Source: [Category-level Object Detection, Pose Estimation and Reconstruction from Stereo Images](<https://www.sota2.com/research/paper/68fa74221e209131cea5fdc4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:02:53.994Z

Authors: Chuanrui Zhang, Yonggen Ling, Minglei Lu, Minghan Qin, Haoqian Wang

Published: 2024-07-09T15:59:03.000Z

## Abstract

We study the 3D object understanding task for manipulating everyday objects with different material properties (diffuse, specular, transparent and mixed). Existing monocular and RGB-D methods suffer from scale ambiguity due to missing or imprecise depth measurements. We present CODERS, a one-stage approach for Category-level Object Detection, pose Estimation and Reconstruction from Stereo images. The base of our pipeline is an implicit stereo matching module that combines stereo image features with 3D position information. Concatenating this presented module and the following transform-decoder architecture leads to end-to-end learning of multiple tasks required by robot manipulation. Our approach significantly outperforms all competing methods in the public TOD dataset. Furthermore, trained on simulated data, CODERS generalize well to unseen category-level object instances in real-world robot manipulation experiments. Our dataset, code, and demos will be available on our project page.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [6D Object Pose Estimation on TOD](<https://www.sota2.com/research/sota/6d-object-pose-estimation-on-tod.md>): 6D Object Pose Estimation; TOD; 10° 10cm Success Rate: 90.8.

- [6D Object Pose Estimation on SS3D](<https://www.sota2.com/research/sota/6d-object-pose-estimation-on-ss3d.md>): 6D Object Pose Estimation; SS3D; 3D50 Accuracy: 61.7.

- [Category-level 6D object pose estimation on LVS6D Easy](<https://www.sota2.com/research/sota/category-level-6d-object-pose-estimation-on-lvs6d-easy.md>): Category-level 6D Object Pose Estimation; LVS6D Easy; AP: 10.2.

- [Category-level 6D object pose estimation on LVS6D Medium](<https://www.sota2.com/research/sota/category-level-6d-object-pose-estimation-on-lvs6d-medium.md>): Category-level 6D Object Pose Estimation; LVS6D Medium; AP: 11.2.

- [Category-level 6D object pose estimation on LVS6D (Hard)](<https://www.sota2.com/research/sota/category-level-6d-object-pose-estimation-on-lvs6d-hard.md>): Category-level 6D Object Pose Estimation; LVS6D (Hard); AP: 7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.06984>)

[PDF](<https://arxiv.org/pdf/2407.06984.pdf>)