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🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)
The main features of qingyonghu/randla-net are: Scene Understanding, Segmentation and Classification, Segmentation and Parsing, Semantic Segmentation, Point Cloud Processing.
Projects with overlapping indexed features include: pqhieu/jsis3d. laughtervv/sgpn — SGPN:Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation, CVPR, 2018. fyu/dilation — Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the… edwardzhou130/polarseg — Implementation for PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (CVPR… hku-mars/fast_lio — FAST_LIO is a real-time SLAM system and LiDAR-inertial odometry package designed for simultaneous localization and… cgal/cgal — CGAL is a software library that provides a comprehensive collection of computational geometry algorithms and data…
SGPN:Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation, CVPR, 2018
Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the network definitions and the trained models. You can use this code together with vanilla Caffe to segment images using the pre-trained models. If you want to train the models yourself,…
Implementation for PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (CVPR 2020)