@inproceedings{
luo2024ldcnet,
title={{LDCN}et: Long-Distance Context Modeling for Large-Scale 3D Point Cloud Scene Semantic Segmentation},
author={Shoutong Luo and Zhengxing Sun and Yi Wang and Yunhan Sun and Chendi Zhu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=r8QjbxK6TW}
}
Large-scale point cloud semantic segmentation is a challenging task in 3D computer vision. A key challenge is how to resolve ambiguities arising from locally high inter-class similarity. In this study, we introduce a solution by modeling long-distance contextual information to understand the scene's overall layout. The context sensitivity of previous methods is typically constrained to small blocks(e.g. $2m \times 2m$) and cannot be directly extended to the entire scene. For this reason, we propose \textbf{L}ong-\textbf{D}istance \textbf{C}ontext Modeling Network(LDCNet). Our key insight is that keypoints are enough for inferring the layout of a scene. Therefore, we represent the entire scene using keypoints along with local descriptors and model long-distance context on these keypoints. Finally, we propagate the long-distance context information from keypoints back to non-keypoints. This allows our method to model long-distance context effectively. We conducted experiments on six datasets, demonstrating that our approach can effectively mitigate ambiguities. Our method performs well on large, irregular objects and exhibits good generalization for typical scenarios.
LDCNet: Long-Distance Context Modeling for Large-Scale 3D Point Cloud Scene Semantic Segmentation
Shoutong Luo Zhengxing Sun Yi Wang Yunhan Sun Chendi Zhu