Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation

• URL: http://arxiv.org/abs/2405.10557v1
• Date: Fri, 17 May 2024 05:48:56 GMT
• Title: Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation
• Authors: Yongliang Lin, Yongzhi Su, Sandeep Inuganti, Yan Di, Naeem Ajilforoushan, Hanqing Yang, Yu Zhang, Jason Rambach,
• Abstract summary: We propose SymCode, a symmetry-aware surface that encodes the object surface based on one-to-many correspondences. We also introduce SymNet, a fast end-to-end network that directly regresses an object's 6D pose parameters without solving a problem.
• Score: 5.821462441570274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the 6D pose of an object from a single RGB image is a critical task that becomes additionally challenging when dealing with symmetric objects. Recent approaches typically establish one-to-one correspondences between image pixels and 3D object surface vertices. However, the utilization of one-to-one correspondences introduces ambiguity for symmetric objects. To address this, we propose SymCode, a symmetry-aware surface encoding that encodes the object surface vertices based on one-to-many correspondences, eliminating the problem of one-to-one correspondence ambiguity. We also introduce SymNet, a fast end-to-end network that directly regresses the 6D pose parameters without solving a PnP problem. We demonstrate faster runtime and comparable accuracy achieved by our method on the T-LESS and IC-BIN benchmarks of mostly symmetric objects. Our source code will be released upon acceptance.

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