Dash Robotics Perception
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Updated
May 8, 2019 - C++
Dash Robotics Perception
Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving
Minimum code needed to run Autoware multi-object tracking
This is a repository mainly about IEEE data fusion contest 2019 track 4 — Cloud points classification
In this project we detect, segment and track the obstacles of an ego car and its custom implementation of KDTree, obstacle detection, segmentation, clustering and tracking algorithm in C++ and compare it to the inbuilt algorithm functions of PCL library on a LiDAR's point cloud data.
Sensor Fusion Nanodegree | Lidar Obstacle Detection in Autonomous Vehicles
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.
ICCV 2021 papers and code focus on point cloud analysis
Trying to compute the completeness of a 3D map and compare it to another 3D map in a pointcloud format
Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features
This repo includes work on lidar point cloud semantic segmentation using self-collected Carla simulator dataset and Semantic KITTI real-world dataset.
Scanning scene point cloud foreground and background segmentation dataset.
Point cloud segmentation with Azure Kinect
Improved pytorch implementation of RandLA (https://arxiv.org/abs/1911.11236) with easier transferability and reproductibility
Multiple command line utilities for working with tree point clouds, e.g. for computing the boxcounting dimension from point cloud data
Submissions for each practical work done in the context of the NPM3D course of the MVA.
A list of papers and datasets about point cloud analysis (processing)
PCD annotation processing and guideline based on Semantic Segmentation Editor
Python scripts for converting mesh formats, mesh simplification and mesh rigid transformation
The research project based on Semantic KITTTI dataset, 3d Point Cloud Segmentation , Obstacle Detection
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