Submission: MRC-Net/ITODD/PBR(DefaultDetections2022)

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Submission name PBR(DefaultDetections2022)
Submission time (UTC) March 18, 2024, 8:50 p.m.
User ymao
Task Model-based 6D localization of seen objects
Dataset ITODD
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
AR:0.393
AR_MSPD:0.532
AR_MSSD:0.353
AR_VSD:0.296
average_time_per_image:2.417

Method: MRC-Net

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