The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Google Research

面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版

PRML algorithms implemented in Python

Code release for NeRF (Neural Radiance Fields)

links to conference publications in graph-based deep learning

[ICCV 2019] Monocular depth estimation from a single image

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse m…

NeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning

Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

(CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

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.

A Scalable Pipeline for Making Steerable Multi-Task Mid-Level Vision Datasets from 3D Scans [ICCV 2021]

[AAAI 2021] HR-Depth : High Resolution Self-Supervised Depth Estimation

Fast Depth Densification for Occlusion-Aware Augmented Reality

DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences. J.Lamarca, S. Parashar, A. Bartoli & J.M.M. Montiel

Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications (IROS 2020)

RVMDE : Radar Validated Monocular Depth Estimation for Robotics