A fast and memory-efficient libarary for sparse transformer with varying token numbers (e.g., 3D point cloud).
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Updated
Sep 6, 2023 - Python
A fast and memory-efficient libarary for sparse transformer with varying token numbers (e.g., 3D point cloud).
This is the official repository of the original Point Transformer architecture.
Fast Segmentation of 3D Point Clouds A Paradigm on LiDAR Data for Autonomous Vehicle Applications
Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter (ECCV 2022)
Extended Kalman Filter and Deep Learning to detect vehicles from RGB and LiDAR data (Sensor Fusion and Tracking project of the Udacity Self-Driving Car Engineer Nanodegree Program)
This is the implementation of Recycle Maxpooling Module for Point Cloud Analysis
The official implementation code of Paper "PointCVaR: Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification" in AAAI 2024 (Oral)
Course submission material for Sensor Fusion and Camera based tracking using Extended Kalman Filters for Udacity Self Driving Nanodegree.
3D point cloud data (npy file) plot(viewer) in python and mayavi.
A tutorial for learning the knowledge and techniques about 3D point clouds.
A PyTorch implementation of Point Transformer that can handle the input data in batch mode.
Feature Extraction, Image Registration, 3D reconstruction from scratch
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