| Submission name | PBR(DefaultDetections2022) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Submission time (UTC) | March 18, 2024, 8:49 p.m. | ||||||||||
| User | ymao | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | YCB-V | ||||||||||
| Training model type | Default | ||||||||||
| Training image type | Synthetic (only PBR images provided for BOP Challenge 2020 were used) | ||||||||||
| Description | One model for all objects, 2D bounding box: BOP 2022 default detections, Official implementation of our CVPR 2024 paper "MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation", Contributors: Yuelong Li, Yafei Mao, Raja Bala, Sunil Hadap | ||||||||||
| Evaluation scores |
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| User | ymao |
|---|---|
| Publication | Yuelong Li*, Yafei Mao*, Raja Bala, Sunil Hadap: MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation, CVPR 2024. |
| Implementation | https://github.com/amzn/mrc-net-6d-pose |
| Training image modalities | RGB |
| Test image modalities | RGB |
| Description | MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation, CVPR 2024, https://arxiv.org/abs/2403.08019, MRC-Net is a simple correspondence-free RGB-only 6DoF pose estimation model with leading accuracy and near real-time speed! It reformulates the conventional multitask classification+regression approach into a new sequential design. The two-sequential stages are bridged by a novel multi-scale correlation structure. |
| Computer specifications | Nvidia V100 |