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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
This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and
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 ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"
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.
The main features of angeladai/3dmv are: 3D Detection and Segmentation, Computer Vision Research.
Projects with overlapping indexed features include: yanx27/pointnet_pointnet2_pytorch — This project is a PyTorch-based framework of deep learning models designed for the classification and semantic… zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… intel-isl/open3d — Open3D is a 3D data processing library, visualization engine, and machine learning library. It provides a framework… abhimanyudubey/confusion — Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification". ahangchen/tfusion — CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns. agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018.