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3dflow

Codes for ECCV2022 paper "What matters in supervised 3D scene flow"

awesome-3d-object-detection-for-autonomous-driving

Papers on 3D Object Detection for Autonomous Driving

bicyclegan

Toward Multimodal Image-to-Image Translation

carla-dataset-runner

Data collector for CARLA 0.9.6

csm

The C(canonical) Scan Matcher

dca-srsfe

This is the code related to "Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds" (CVPR 2022).

di-drive

OpenDILab Auto-driving platform

di-engine

OpenDILab Decision AI Engine

diffindscene

dso

Direct Sparse Odometry

fastflow3d

Implementation of the FastFlow3D architecture for scene flow estimation from LiDAR point clouds in PyTorch using PyTorch Lightning.

flot

FLOT: Scene Flow Estimation by Learned Optimal Transport on Point Clouds

l3net_redeploy

private

lanyon

A content-first, sliding sidebar theme for Jekyll.

learningodomerrormodel

loadladybugpgr

meta-sim

Meta-Sim: Learning to Generate Synthetic Datasets (ICCV 2019)

misgan

MisGAN: Learning from Incomplete Data with GANs

ndt_omp

Multi-threaded and SSE friendly NDT algorithm

octomap

An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Contains the main OctoMap library, the viewer octovis, and dynamicEDT3D.

odometry_utils

once_benchmark

One Million Scenes for Autonomous Driving

openpcdet

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

openroaded

modified from an opensource project

perception_imitator

This is the implementation for our paper.

pptk

The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.

pyramid-rcnn

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

pytorch-a2c-ppo-acktr

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO) and Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR).

pytorch-memory-utils

pytorch memory track code

rlll