Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors

Pytorch framework for doing deep learning on point clouds.

Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence.

Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions (RAL 2022)

My implementation of Graspnet Graspness.

Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation (RAL 2023)

MINSU3D: MinkowskiEngine-powered Scene Understanding in 3D

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The code contains updated files to sucessfully compile Minkowski engine to latest cuda and pytorch versions

The four major frameworks for 3D point cloud sparse acceleration, which are currently mainstream, are compared. These include MIT-HAN-LAB's torchsparse, NVIDIA's MinkowskiEngine, TuSimple's spconv, and Facebook Research's SparseConvNet.

Benchmarking various sparse convolution libraries: MinkowskiEngine, SpConv, TorchSparse, and Open3D.

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Panoptic segmentation baseline implemented based on the MinkowskiEngine library

Installation Guide for Windows Subsystem for Linux 2

A customizable Dockerfile that allows you to specify Ubuntu, CUDA, Python, and PyTorch versions for installing Minkowski Engine

Fix build issues for CUDA 12.2 and gcc-12

a docker environment for Minkowski Engine and SPConv

Deep learning based LArTPC event reconstruction using MinkowskiEngine sparse convolution APIs.

forked repo from https://github.com/StanfordVL/MinkowskiEngine, need stable version for prototype.

Attempt to use MinkowskiEngine (failed)

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Easy to install

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Prebuilt CUDA MinkowskiEngine wheels for HPC/Apptainer environments.