NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds
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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.
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29 more
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Built on by
Citing papers per year: 2023: 4; 2024: 6; 2025: 2.
12 papers.
- 2023OneFormer3D: One Transformer for Unified Point Cloud Segmentationcited by 95
- 2023Top-Down Beats Bottom-Up in 3D Instance Segmentationcited by 31
- 2024A Unified Framework for 3D Scene Understandingcited by 15
- 2023Structured Epipolar Matcher for Local Feature Matchingcited by 8
- 2024MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentationcited by 4
- 2024CompetitorFormer: Competitor Transformer for 3D Instance Segmentationcited by 4
- 2025All in One: Visual-Description-Guided Unified Point Cloud Segmentationcited by 4
- 2024ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Methodcited by 2
- 2024Joint prototype and coefficient prediction for 3D instance segmentationcited by 1
- 2025Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gapcited by 1
- 2023Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labelscited by 0
- 2024Instance Segmentation of Reinforced Concrete Bridges with Synthetic Point Cloudscited by 0
- 2025Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap8 Sep 2025
- 2025All in One: Visual-Description-Guided Unified Point Cloud Segmentation7 Jul 2025
- 2024CompetitorFormer: Competitor Transformer for 3D Instance Segmentation21 Nov 2024
- 2024MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation4 Nov 2024
- 2024ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Method3 Oct 2024
- 2024Instance Segmentation of Reinforced Concrete Bridges with Synthetic Point Clouds24 Sep 2024
- 2024Joint prototype and coefficient prediction for 3D instance segmentation9 Jul 2024
- 2024A Unified Framework for 3D Scene Understanding3 Jul 2024
- 2023Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labels11 Dec 2023
- 2023OneFormer3D: One Transformer for Unified Point Cloud Segmentation24 Nov 2023
- 2023Structured Epipolar Matcher for Local Feature Matching29 Mar 2023
- 2023Top-Down Beats Bottom-Up in 3D Instance Segmentation6 Feb 2023