CurveCloudNet: Processing Point Clouds with 1D Structure
arXiv (Cornell University)(2023)
摘要
Modern depth sensors such as LiDAR operate by sweeping laser-beams across the
scene, resulting in a point cloud with notable 1D curve-like structures. In
this work, we introduce a new point cloud processing scheme and backbone,
called CurveCloudNet, which takes advantage of the curve-like structure
inherent to these sensors. While existing backbones discard the rich 1D
traversal patterns and rely on generic 3D operations, CurveCloudNet
parameterizes the point cloud as a collection of polylines (dubbed a "curve
cloud"), establishing a local surface-aware ordering on the points. By
reasoning along curves, CurveCloudNet captures lightweight curve-aware priors
to efficiently and accurately reason in several diverse 3D environments. We
evaluate CurveCloudNet on multiple synthetic and real datasets that exhibit
distinct 3D size and structure. We demonstrate that CurveCloudNet outperforms
both point-based and sparse-voxel backbones in various segmentation settings,
notably scaling to large scenes better than point-based alternatives while
exhibiting improved single-object performance over sparse-voxel alternatives.
In all, CurveCloudNet is an efficient and accurate backbone that can handle a
larger variety of 3D environments than past works.
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关键词
processing point clouds,point clouds,1d structure
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