GS-Pose: Generalizable Segmentation-Based 6D Object Pose Estimation With 3D Gaussian Splatting

Dingding Cai, Janne Heikkila, Esa Rahtu

Keywords: 6D Object Pose Estimation, Segmentation, 3D Gaussian Splatting

Abstract: This paper introduces GS-Pose, a unified framework for localizing and estimating the 6D pose of novel objects. GS-Pose begins with a set of posed RGB images of a previously unseen object and builds three distinct representations stored in a database. At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial 6D pose using a retrieval approach, and refining the pose with a render-and-compare method. The key insight is the application of the appropriate object representation at each stage of the process. In particular, for the refinement step, we leverage 3D Gaussian splatting, a novel differentiable rendering technique that offers high rendering speed and relatively low optimization time. Off-the-shelf toolchains and commodity hardware, such as mobile phones, can be used to capture new objects to be added to the database. Extensive evaluations on the LINEMOD and OnePose-LowTexture datasets demonstrate excellent performance, establishing the new state-of-the-art. The source code is publicly available at https://github.com/dingdingcai/GSPose.

Submission Number: 144