Submission: Coupled Iterative Refinement/LM-O

Submission name
Submission time (UTC) April 24, 2022, 10:03 p.m.
User lahav
Task Pose estimation (BOP 2019-2022)
Dataset LM-O
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Description Single view. Dataset splits: train_pbr
Evaluation scores

Method: Coupled Iterative Refinement

User lahav
Publication Coupled Iterative Refinement for 6D Multi-Object Pose Estimation, CVPR 2022
Training image modalities RGB-D
Test image modalities RGB-D

We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge. Our approach it- eratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy. We use a novel differentiable layer to perform pose refinement by solving an optimization problem we refer to as Bidirectional Depth-Augmented Perspective- N-Point (BD-PnP).

Computer specifications 2 RTX-3090s