Reducing the Gap Between Spatial and Space-Filling Curve Neighbors in 3D Point Cloud Segmentation
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...
| Authors | Konan Sasaki, Junya Komori, Kazuhiro Hotta |
|---|---|
| Primary category | cs.CV |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 15 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.