Projects sharing features with MeshCNN

Convolutional Recurrent Neural Network

Volumetric CNN (Convolutional Neural Networks) for Object Classification on 3D Data, with Torch implementation.

PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without voxelization. It provides a system for 3D object classification, semantic segmentation frameworks for partitioning clouds into categories, and tools for visualizing 3D shapes. The project utilizes a transform network to align point clouds into a canonical coordinate space and employs symmetric-function-based aggregation to condense point-wise features into global vectors regardless of point order. It also features a multi-scale grouping architecture to extract hierarchical geometric

PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da

3D/Volumetric Convolutional Neural Networks with Theano+Lasagne.

PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 Wenxuan Wu, Zhongang Qi, Li Fuxin.

Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification

Dilated Residual Networks

A memory-efficient implementation of DenseNets

OctNet uses efficient space partitioning structures (i.e. octrees) to reduce memory and compute requirements of 3D convolutional neural networks, thereby enabling deep learning at high resolutions.

This repository is the official implementation of paper: "Unsupervised Point Cloud Pre-training via Occlusion Completion"

Non official pytorch implementation of i-Resnet, invertible residual networks.

This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa

This is a PyTorch-based training pipeline designed for reproducible image classification benchmarking on the CIFAR-10 dataset. It integrates GPU-accelerated computation, data augmentation, learning rate scheduling, and checkpointing to produce consistent accuracy measurements across multiple ResNet architectures. The project distinguishes itself by providing a fixed-architecture benchmark suite that trains a predefined set of ResNet variants, from ResNet18 through ResNet152, on CIFAR-10. It implements a step-based learning rate decay schedule at predetermined epochs to stabilize convergence,

Here you can find the code for the BMVC 2017 version of "Orientation-boosted Voxel Nets for 3D Object Recognition", a.k.a ORION.

Faster RCNN with PyTorch

This repository contains the source code to support the paper: Shape Adaptor: A Learnable Resizing Module, by Shikun Liu and Adobe Research Team.

PyTorch implementation of Deformable Convolution