LiDAR-Net: A Real-scanned 3D Point Cloud Dataset for Indoor Scenes

no code implementations • CVPR 2024 • Yanwen Guo, Yuanqi Li, Dayong Ren, Xiaohong Zhang, Jiawei Li, Liang Pu, Changfeng Ma, Xiaoyu Zhan, Jie Guo, Mingqiang Wei, Yan Zhang, Piaopiao Yu, Shuangyu Yang, Donghao Ji, Huisheng Ye, Hao Sun, Yansong Liu, Yinuo Chen, Jiaqi Zhu, Hongyu Liu

In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3. 6 billion precisely point-level annotated points covering an expansive area of 30000m^2.

Spiking PointNet: Spiking Neural Networks for Point Clouds

1 code implementation • NeurIPS 2023 • Dayong Ren, Zhe Ma, Yuanpei Chen, Weihang Peng, Xiaode Liu, Yuhan Zhang, Yufei Guo

We discover that the two huge obstacles limiting the application of SNNs in point clouds are: the intrinsic optimization obstacle of SNNs that impedes the training of a big spiking model with large time steps, and the expensive memory and computation cost of PointNet that makes training a big spiking point model unrealistic.

GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling

1 code implementation • 14 Jul 2022 • Chen Chen, Yisen Wang, Honghua Chen, Xuefeng Yan, Dayong Ren, Yanwen Guo, Haoran Xie, Fu Lee Wang, Mingqiang Wei

Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding. Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity.

Decoder Object +2