GS-Pose: Cascaded Framework for Generalizable Segmentation-based 6D
  Object Pose Estimation
Dingding Cai Janne Heikkilä Esa Rahtu
This paper introduces GS-Pose, an end-to-end framework for locating and
estimating the 6D pose of 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 utilize 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. Project page: https://dingdingcai.github.io/gs-pose.