In the point-cloud-based place recognition area, the existing hybrid architectures combining both convolutional networks and transformers have shown promising performance. They mainly apply the voxel-wise transformer after the sparse convolution (SPConv). However, they can induce information loss by the...
《Point-wise Convolutional Neural Network》B Hua, M Tran, S Yeung [Singapore University of Technology and Design] (2017) http://t.cn/RT9nJ6i
Geometric transformer for fast and robust point cloud registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 19–24 June 2022; pp. 11143–11152. [Google Scholar] Wu, W.; Qi, Z.; Fuxin, L. PointConv: Deep convolutional ...
Improving 3D Object Detection with Channel-wise Transformer [det; Github] Voxel-based Network for Shape Completion by Leveraging Edge Generation [completion; Github] Exploring Simple 3D Multi-Object Tracking for Autonomous Driving [tracking] ME-PCN: Point Completion Conditioned on Mask Emptiness [complet...
Module I is first designed to construct a revised 3D point-wise convolutional operation. Then, a U-shaped downsampling-upsampling architecture is proposed to leverage both global and local features in multiple scales in Module II. Next, in Module III, high-level local edge features in 3D point...
Our network architecture consists three components: the first is full point-wise convolutional part, which extracts Non-local statistical regularities; the ... S Zhang,F He,W Ren - 《Neurocomputing》 被引量: 0发表: 2020年 A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease ...
[ICCV] Improving 3D Object Detection with Channel-wise Transformer. [Detection] [ICCV] patio-temporal Self-Supervised Representation Learning for 3D Point Clouds. [Learning] [ICCV] Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. [Detection] [ICCV] Voxel Transformer...
21 presented a fully-convolutional geometric feature extraction module which can be computed in a single pass by a 3d fully-convolutional network. These features are compact, capture broad spatial context and scale to large scenes. Li et al. presented VoxFormer22, a Transformer based semantic ...
[arXiv] Spherical Convolutional Neural Network for 3D Point Clouds. [cls.] [arXiv] Adversarial Autoencoders for Generating 3D Point Clouds. [oth.] [arXiv] Iterative Transformer Network for 3D Point Cloud. [cls. seg. pos.] [arXiv] Topology-Aware Surface Reconstruction for Point Clouds. [re...
[arXiv] Spherical Convolutional Neural Network for 3D Point Clouds. [cls.] [arXiv] Adversarial Autoencoders for Generating 3D Point Clouds. [oth.] [arXiv] Iterative Transformer Network for 3D Point Cloud. [cls. seg. pos.] [arXiv] Topology-Aware Surface Reconstruction for Point Clouds. [re...