NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds

• URL: http://arxiv.org/abs/2207.09978v1
• Date: Wed, 20 Jul 2022 15:37:32 GMT
• Title: NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds
• Authors: Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi
• Abstract summary: We introduce a method for instance proposal generation for 3D point clouds. We show that this serves as a critical bottleneck, and propose a method based on iterative bilateral filtering with learned kernels.
• Score: 44.258500431460924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a method for instance proposal generation for 3D point clouds. Existing techniques typically directly regress proposals in a single feed-forward step, leading to inaccurate estimation. We show that this serves as a critical bottleneck, and propose a method based on iterative bilateral filtering with learned kernels. Following the spirit of bilateral filtering, we consider both the deep feature embeddings of each point, as well as their locations in the 3D space. We show via synthetic experiments that our method brings drastic improvements when generating instance proposals for a given point of interest. We further validate our method on the challenging ScanNet benchmark, achieving the best instance segmentation performance amongst the sub-category of top-down methods.

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