Coupled Iterative Refinement for 6D Multi-Object Pose Estimation

• URL: http://arxiv.org/abs/2204.12516v1
• Date: Tue, 26 Apr 2022 18:00:08 GMT
• Title: Coupled Iterative Refinement for 6D Multi-Object Pose Estimation
• Authors: Lahav Lipson, Zachary Teed, Ankit Goyal, Jia Deng
• Abstract summary: Given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy.
• Score: 64.7198752089041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the task of 6D multi-object pose: given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. 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 iteratively 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). Our method achieves state-of-the-art accuracy on standard 6D Object Pose benchmarks. Code is available at https://github.com/princeton-vl/Coupled-Iterative-Refinement.

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