Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation
This addresses a critical challenge in computer vision for robotics and AR/VR applications, offering an incremental improvement over existing correspondence-based methods.
The paper tackles the problem of 6D object pose estimation from a single RGB image, particularly for symmetric objects, by proposing SymCode and SymNet to eliminate ambiguity and achieve faster runtime with comparable accuracy on benchmarks like T-LESS and IC-BIN.
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.