Submission: Coupled Iterative Refinement (RGB)/LM-O

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Submission name
Submission time (UTC) April 24, 2022, 10:39 p.m.
User lahav
Task Model-based 6D localization of seen objects
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 (RGB)

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

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