mlsanityData to 14 Oct 2026

Reducing the Gap Between Spatial and Space-Filling Curve Neighbors in 3D Point Cloud Segmentation

arXiv 2610.19530 ↗First posted 14 Oct 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

As a lightweight and highly accurate method for nearest-neighbor search in 3D point clouds, approaches based on space-filling curves (SFC) have been proposed. However, due to the inherent properties of SFC, a gap arises between proximity relationships along the SFC and those in the original Euclidean space. In this study, we propose a loss function that reduces the influence of this gap. Specifically, we compute the distances between adjacent points along the SFC and train the model to optimize feature similarity between pairs of points that are distant from each other in the original 3D space. We incorporate the proposed method into a high-performing 3D point cloud segmentation model and evaluate its effectiveness on three datasets. The results demonstrate that the proposed method outperforms conventional methods on both indoor and outdoor point cloud datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 31 references that are in the map. First posted on arXiv 14 Oct 2026.

Builds on

23 of 31 references.

References · 23

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  4. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  6. 2020Point Transformercited by 1,470
  7. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
  8. 2020PCT: Point cloud transformercited by 732
  9. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  10. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  11. 2016Semantic Scene Completion from a Single Depth Imagecited by 472
  12. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
11 more
  1. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  2. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  3. 2020Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Reviewcited by 116
  4. 2023Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understandingcited by 113
  5. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 109
  6. 2016Vehicle Detection from 3D Lidar Using Fully Convolutional Networkcited by 107
  7. 2023FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformercited by 73
  8. 2021Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusioncited by 72
  9. 2020DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentationcited by 66
  10. 2023Efficient 3D Semantic Segmentation with Superpoint Transformercited by 57
  11. 2023PanopticNDT: Efficient and Robust Panoptic Mappingcited by 6

Built on by

Not cited yet.