mlsanityData to 7 Oct 2026

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

arXiv 2207.09978 ↗First posted 20 Jul 2022cs.CV cs.GRcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 41 of its 55 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

41 of 55 references.

References · 41

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29 more
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Citing papers per year: 2023: 4; 2024: 6; 2025: 2.

12 papers.