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: this https URL

GS-Pose 是一个端到端的定位和估计物体 6D 姿态的框架，通过构建三种不同的表示形式，利用逐步定位、检索方法和渲染比较方法，以适应各个阶段的合适物体表示，并利用可用的工具链和常见硬件进行新对象的数据捕捉和数据库添加。在 LINEMOD 和 OnePose-LowTexture 数据集上进行广泛评估，取得了卓越的性能，建立了新的最先进技术。