224 Open source projects that are alternatives of or similar to frustum-convnet

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

Edge-detection application with PointCloud Library

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

Implementations of a rather simple version of the Iterative Closest Point algorithm in various languages.

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

A lightweight tool for labeling 3D bounding boxes in point clouds.

3D Graph Neural Networks for RGBD Semantic Segmentation

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

L-CAS 3D Point Cloud Annotation Tool

ECCV 2020 papers focusing on point cloud analysis

A hackable lidar viewer

No description or website provided.

The open source mesh processing system

Creating real-time dynamic Point Clouds using PyQtGraph, Kinect 2 and the python library PyKinect2.

A high-performance neural network library for point cloud processing.

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

Point cloud semantic segmentation via Deep 3D Convolutional Neural Network

"From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder" and "Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud" in ACM MM 2021.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Efficient 3D Deep Learning

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）

Fast Descriptors and Correspondence Propagation for Robust Global Point Cloud Registration

A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)

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

This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.

[ACM MM 2022] You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

Grid-GCN for Fast and Scalable Point Cloud Learning

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

Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)

Official PyTorch code for the paper: "Point-Based Modeling of Human Clothing" (ICCV 2021)

Patch-base progressive 3D Point Set Upsampling

Experiments on unsupervised point cloud reconstruction.

Lightweight and Accurate Point Cloud Clustering

Official version of 'Weakly Supervised 3D object detection from Lidar Point Cloud'(ECCV2020)

Cluster based segmentation of Point Cloud with PCL lib in ROS

Julia bindings for the displaz lidar viewer

A learned sampling approach for point clouds (CVPR 2019)

CVPR 2020 papers focusing on point cloud analysis

open Multi-View Stereo reconstruction library

Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

FPConv: Learning Local Flattening for Point Convolution, CVPR 2020

Paper list of deep learning on point clouds.

[CVPR 2021, Oral] PREDATOR: Registration of 3D Point Clouds with Low Overlap.

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

Stream openni2 depth images over the network

Pytorch implementation of 'Graph Attention Convolution for Point Cloud Segmentation'

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

User-friendly, commercial-grade software for processing aerial imagery. 🛩

Remap, mask, renumber, unique, and in-place transposition of 3D labeled images. Point cloud too.

A Laser-Camera Calibration Toolbox extending from that at http://www.cs.cmu.edu/~ranjith/lcct.html

Robotics with GPU computing

DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization

python codes for remote sensing applications will be uploaded here. I will try to teach everything I learn during my projects in here.

Create Dense Depth Map Image for Known Poisitioned Camera from Lidar Point Cloud

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

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

LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis, ICCV 2019, Seoul, Korea

Visualising LIDAR data from KITTI dataset.

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