569 Open source projects that are alternatives of or similar to Displaz

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

Python bindings for the Point Cloud Library (PCL)

WhiteboxTools Python Frontend

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

R package for airborne LiDAR data manipulation and visualisation for forestry application

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

Julia bindings for the displaz lidar viewer

ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"

An interactive GUI for WhiteboxTools in a Jupyter-based environment

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

ArcGIS Python Toolbox for WhiteboxTools

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

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

🚕 Fast and robust clustering of point clouds generated with a Velodyne sensor.

KITTI data processing and 3D CNN for Vehicle Detection

Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

NCSU GIS595/MEA792: UAV/lidar Data Analytics

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

Weakly Supervised 3D Object Detection from Point Clouds (VS3D), ACM MM 2020

LiDAR fog simulation

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

😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks

3D LIDAR-based Graph SLAM

Specification for streaming massive heterogeneous 3D geospatial datasets 🌎

[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory

Research platform for 3D object detection in PyTorch.

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

Interactive Map Correction for 3D Graph SLAM

Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.

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.

geospatial xarray extension powered by rasterio

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

[CVPR'19] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds

Python Geohash Compression Tool

GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more

A component of MapFish for printing templated cartographic maps. This module is the Java serverside module. For support post to the mailing list: https://groups.google.com/forum/#!forum/mapfish-print-users

PyTorch Implementation of PIXOR

The Open Source Geospatial Foundation is not-for-profit organization to empower everyone with open source geospatial. Directly supports projects as an outreach and advocacy organization providing financial, organizational and legal support. Works with our sponsors and partners for open software, standards, data, research and education.

Python binding of 3D visualization library Pangolin

Grid-GCN for Fast and Scalable Point Cloud Learning

pycsw is an OGC CSW server implementation written in Python. pycsw fully implements the OpenGIS Catalogue Service Implementation Specification [Catalogue Service for the Web]. Initial development started in 2010 (more formally announced in 2011). The project is certified OGC Compliant, and is an OGC Reference Implementation. pycsw allows for the publishing and discovery of geospatial metadata via numerous APIs (CSW 2/CSW 3, OpenSearch, OAI-PMH, SRU). Existing repositories of geospatial metadata can also be exposed, providing a standards-based metadata and catalogue component of spatial data infrastructures. pycsw is Open Source, released under an MIT license, and runs on all major platforms (Windows, Linux, Mac OS X). Please read the docs at https://pycsw.org/docs for more information.

Differentiable Surface Splatting

Point cloud semantic segmentation via Deep 3D Convolutional Neural Network

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

code/webpage for the DeepMapping project

.NET Spatial Reference and Projection Engine

The Raster Foundry web application.

Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

GeoTrellis for PySpark

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Self-contained examples for Maps API for JavaScript v3.

Full-python LiDAR SLAM using ICP and Scan Context

Mirror of Apache Calcite - Avatica

CloudCompare main repository

The open source mesh processing system

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

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Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving