385 Open source projects that are alternatives of or similar to NeuralPull

open Multi-View Stereo reconstruction library

pytorch implementation of >>Patch-base progressive 3D Point Set Upsampling<<

SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification - ECCV 2020 oral

PyTorch Code of our WACV2020 paper: Silhouette Guided Point Cloud Reconstruction beyond Occlusion

Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)

A resource repository for 3D machine learning

Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020)

A python tool for fitting primitives 3D shapes in point clouds using RANSAC algorithm

Code for PCN: Point Completion Network in 3DV'18 (Oral)

A list of resources about deep learning solutions on 3D shape processing

The open source mesh processing system

[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

Code for our paper, Neural Network Attributions: A Causal Perspective (ICML 2019).

Graduation Project: A point cloud semantic segmentation and VIO based 3D reconstruction method using RGB-D and IMU

Code for ECCV 2020 "DVI: Depth Guided Video Inpainting for Autonomous Driving"

Complete download for papers in various top conferences

API to support AIST++ Dataset: https://google.github.io/aistplusplus_dataset

This demo uses a deep neural network and two generic cameras to perform 3D pose estimation.

FedScale is a scalable and extensible open-source federated learning (FL) platform.

[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

Associatively Segmenting Instances and Semantics in Point Clouds, CVPR 2019

OpenCV .Net application supporting several RGBD cameras - Kinect, Intel RealSense, Luxonis Oak-D, Mynt Eye D 1000, and StereoLabs ZED 2

Geometric Adversarial Attacks and Defenses on 3D Point Clouds (3DV 2021)

Experiments on unsupervised point cloud reconstruction.

Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)

A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

register 3D point clouds using rotation, translation, and scale transformations.

This project is to convert PortraitFCN+ (by Xiaoyong Shen) from Matlab to Tensorflow, then refine the outputs from it (converted to a trimap) using KNN and ResNet, supervised by Richard Berwick.

[ICCV 2021] Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural radiance fields for view synthesis.

Global L0 algorithm for regularity-constrained plane fitting

Automatically registers (aligns) and visualizes point clouds, or processes a whole bunch at once

Papers, authors and author affiliations from ICML, NeurIPS and ICLR 2006-2021

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Motion Averaging

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)

FlowNet3D: Learning Scene Flow in 3D Point Clouds (CVPR 2019)

Multi-view 3D reconstruction using neural rendering. Unofficial implementation of UNISURF, VolSDF, NeuS and more.

No description or website provided.

Robotics with GPU computing

Templated python inferface for Point Cloud Library (PCL) based on Cython

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition

our code for paper 'PointCutMix: Regularization Strategy for Point Cloud Classification'

Visualising LIDAR data from KITTI dataset.

A Collection of Algorithms for Relative Pose Estimation of a Calibrated Camera

The public CGAL repository, see the README below

Python bindings for the Point Cloud Library (PCL)

A readable implementation of structure-from-motion

Differentiable Point Cloud Sampling (CVPR 2020 Oral)

C++ library and programs for reading and writing ASPRS LAS format with LiDAR data

30th June, 2019 - 30th June, 2020. Robotics and Machine Intelligence Lab, The Hong Kong Polytechnic University. This work is supported in part by the Chinese National Engineering Research Centre for Steel Construction (Hong Kong Branch) at The Hong Kong Polytechnic University under grant BBV8, in part by the Research Grants Council of Hong Kong …

Efficient 3D Deep Learning

Julia bindings for the displaz lidar viewer

Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)

The PyTorch Implementation of F-ConvNet for 3D Object Detection

[CVPR 2021] Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis (official pytorch implementation)

Implementation of the Point Transformer layer, in Pytorch

A ground segmentation algorithm for 3D point clouds based on the work described in “Fast segmentation of 3D point clouds: a paradigm on LIDAR data for Autonomous Vehicle Applications”, D. Zermas, I. Izzat and N. Papanikolopoulos, 2017. Distinguish between road and non-road points. Road surface extraction. Plane fit ground filter