Segment Anything in 3D Gaussians
arxiv(2024)
摘要
3D Gaussian Splatting has emerged as an alternative 3D representation of
Neural Radiance Fields (NeRFs), benefiting from its high-quality rendering
results and real-time rendering speed. Considering the 3D Gaussian
representation remains unparsed, it is necessary first to execute object
segmentation within this domain. Subsequently, scene editing and collision
detection can be performed, proving vital to a multitude of applications, such
as virtual reality (VR), augmented reality (AR), game/movie production, etc. In
this paper, we propose a novel approach to achieve object segmentation in 3D
Gaussian via an interactive procedure without any training process and learned
parameters. We refer to the proposed method as SA-GS, for Segment Anything in
3D Gaussians. Given a set of clicked points in a single input view, SA-GS can
generalize SAM to achieve 3D consistent segmentation via the proposed
multi-view mask generation and view-wise label assignment methods. We also
propose a cross-view label-voting approach to assign labels from different
views. In addition, in order to address the boundary roughness issue of
segmented objects resulting from the non-negligible spatial sizes of 3D
Gaussian located at the boundary, SA-GS incorporates the simple but effective
Gaussian Decomposition scheme. Extensive experiments demonstrate that SA-GS
achieves high-quality 3D segmentation results, which can also be easily applied
for scene editing and collision detection tasks. Codes will be released soon.
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