midjourney in discord api.

Simple Viser Viewer for 2D Gaussian Splatting for Geometrically Accurate Radiance Fields

Xtreme1 - The Next GEN Platform for Multimodal Training Data. #3D annotation, 3D segmentation, lidar-camera fusion annotation, image annotation and rlhf tools are supported!

A wheel-mounted MEMS IMU-based dead reckoning system.

LiDAR SLAM = FAST-LIO + Scan Context

Faster-LIO: Lightweight Tightly Coupled Lidar-inertial Odometry using Parallel Sparse Incremental Voxels

TRI-ML Monocular Depth Estimation Repository

Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency (AAAI 2021)

VINS-Fusion based visual-inertial SLAM with tightly-coupled wheel odometry for outdoor delivery robot

cascade-stereo

VOLDOR-SLAM is a real-time dense-indirect SLAM system takes dense optical flows as input that supports monocular, stereo and RGB-D video sequence.

Mirror of the Basalt repository. All pull requests and issues should be sent to https://gitlab.com/VladyslavUsenko/basalt

Direct Sparse Mapping

COCO API - Dataset @ http://cocodataset.org/

LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

A fast and robust point cloud registration library

视觉(语义) SLAM 相关研究跟踪

This is the project page of the paper "Flow-Motion and Depth Network for Monocular Stereo and Beyond''

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

ORB-SLAM2 源码注释， 基于泡泡机器人的注释版本

loam code noted in Chinese（loam中文注解版）

[Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

This is the open-source version of ICRA 2019 submission "Real-time Scalable Dense Surfel Mapping"

Direct sparse odometry combined with stereo cameras and IMU

Master's Thesis on Simultaneous Localization and Mapping in dynamic environments. Separately reconstructs both the static environment and the dynamic objects from it, such as cars.

Multiple hypothesis joint compatibility branch and bound (MHJCBB)