SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints

31 March 2020

Papers citing "SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints"

6 / 6 papers shown

Title
3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion Yixuan Li Weidong Yang Ben Fei 27 9 0 10 Apr 2024
D3Former: Jointly Learning Repeatable Dense Detectors and Feature-enhanced Descriptors via Saliency-guided Transformer Junjie Gao Pengfei Wang Qiujie Dong Qiong Zeng Shiqing Xin Caiming Zhang 19 0 0 20 Dec 2023
OAAFormer: Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer Junjie Gao Qiujie Dong Ruian Wang Shuangmin Chen Shiqing Xin Changhe Tu Wenping Wang 31 1 0 15 Oct 2023
Local region-learning modules for point cloud classification Kaya Turgut H. Dutagaci 3DPC 19 3 0 30 Mar 2023
LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling Xu Wang Yi Jin Yigang Cen Tao Wang Bowen Tang Yidong Li ViT 16 28 0 13 Feb 2022
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules Xin Wen Zhizhong Han Xinhai Liu Yu-Shen Liu 3DPC 13 38 0 29 Aug 2019