Blender Python PLY importer for point clouds and nonstandard models.
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Updated
Nov 13, 2023
Blender Python PLY importer for point clouds and nonstandard models.
Source code for: Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds), accepted at ACCV 2018
Configurable point cloud registration pipeline.
A fast and simple method for multi-planes detection from point cloud
A unified library for 3D data processing with both c++ and python API
This is a software for finely removing non-ground points from point clouds.
A unified library for fitting primitives from 3D point cloud data with both C++&Python API.
ROS package for stereo matching and point cloud projection using AANet (Adaptive Aggregation Network for Efficient Stereo Matching, CVPR 2020)
MandelBrot Fractal Explorer
Parallel LiDAR Point Cloud Preprocessing for Autonomous Driving Applications
Minimal API for obtaining PCL pointcloud using Intel realsense camera.
Lidar project for obstacle detection using PointCloud library.
A repository for a set of perception and robotic vision modules, developed by students in Vortex NTNU for use in the AUV and ASV software stacks.
3ِD Change Detection In Point Cloud
The "Knowledge-based object Detection in Image and Point cloud" (KnowDIP) project aims at the conception of a framework for automatic object detection in unstructured and heterogeneous data. This framework uses a representation of human knowledge in order to improve the flexibility, accuracy, and efficiency of data processing.
This is the implementation for the reconstruction of 3D scans. These uses multiple algorithms to scale and reconstruct the point cloud in order to obtain valid results
[GMP2024 & CAGD] PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis
A useful tool to cut a set of point cloud into two parts with a designed IoU (overlapping)
Implementing a PointNet based architecture for classification and segmentation with point clouds. Q1 and Q2 focus on implementing, training and testing models. Q3 asks you to quantitatively analyze model robustness.
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