FSC: Few-point Shape Completion
CVPR 2024(2024)
Abstract
While previous studies have demonstrated successful 3D object shape
completion with a sufficient number of points, they often fail in scenarios
when a few points, e.g. tens of points, are observed. Surprisingly, via entropy
analysis, we find that even a few points, e.g. 64 points, could retain
substantial information to help recover the 3D shape of the object. To address
the challenge of shape completion with very sparse point clouds, we then
propose Few-point Shape Completion (FSC) model, which contains a novel
dual-branch feature extractor for handling extremely sparse inputs, coupled
with an extensive branch for maximal point utilization with a saliency branch
for dynamic importance assignment. This model is further bolstered by a
two-stage revision network that refines both the extracted features and the
decoder output, enhancing the detail and authenticity of the completed point
cloud. Our experiments demonstrate the feasibility of recovering 3D shapes from
a few points. The proposed FSC (FSC) model outperforms previous methods on both
few-point inputs and many-point inputs, and shows good generalizability to
different object categories.
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