This is the official implementation of DSGN (CVPR 2020), a strong 3D object detector proposed to jointly estimate scene depth and detect 3D objects in 3D world with only input of a stereo image pair.
Principalele funcționalități ale chenyilun95/dsgn sunt: 3D Detection and Segmentation.
Alternativele open-source pentru chenyilun95/dsgn includ: yanx27/pointnet_pointnet2_pytorch — This project is a PyTorch-based framework of deep learning models designed for the classification and semantic… intel-isl/open3d — Open3D is a 3D data processing library, visualization engine, and machine learning library. It provides a framework… aim-uofa/dyco3d — Code for the paper DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution, CVPR 2021. anonymous0522/raan — 0. The CUDA and Pytorch version that is used for this work: ~~~ 'CUDA==10.0', 'torch==1.1.0', 'CUDNN==7.5.0' ~~~… autonomousvision/neat — This repository is for the ICCV 2021 paper NEAT: Neural Attention Fields for End-to-End Autonomous Driving. angeladai/3dmv — 3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This…
This project is a PyTorch-based framework of deep learning models designed for the classification and semantic segmentation of 3D point cloud data. It provides implementations of the PointNet architecture to perform global category labeling of entire objects and detailed partitioning of large-scale 3D environments. The system handles semantic segmentation across multiple scales, ranging from identifying individual components within a single object to labeling distinct category types within large-scale scenes. The framework includes structural components for processing unordered point sets, s
Open3D is a 3D data processing library, visualization engine, and machine learning library. It provides a framework for manipulating point clouds and meshes through specialized algorithms designed for 3D data science workflows. The project includes a toolkit for 3D scene reconstruction to generate spatial models and align surfaces from raw data. It also functions as a GPU accelerated framework that offloads intensive spatial computations to the graphics processor to increase processing speed. The library covers a broad range of capabilities including physically based light simulations for vi
Code for the paper DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution, CVPR 2021.
3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 paper, 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation.