Publikation
Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation
Yongliang Lin; Yongzhi Su; Sandeep Prudhvi Krishna Inuganti; Yan Di; Naeem Ajiforoushan; Hanqing Yang; Yu Zhang; Jason Raphael Rambach
In: IEEE Transactions on Image Processing (TIP), IEEE, 3/2025.
Zusammenfassung
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. The code is available at https://github.com/lyltc1/SymNet.