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[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation
CUDA accelerated rasterization of gaussian splatting
A 3DGS framework for omni urban scene reconstruction and simulation.
[ECCV 2024] Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
[SIGGRAPH'24] 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
tutorial for writing custom pytorch cpp+cuda kernel, applied on volume rendering (NeRF)
[CVPR'24 Best Student Paper] Mip-Splatting: Alias-free 3D Gaussian Splatting
A Modular Framework for 3D Gaussian Splatting and Beyond
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
A detailed formulae explanation on gaussian splatting
Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.
Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 2023)
Tracking and collecting papers/projects/others related to Segment Anything.
Split screen video comparison tool using FFmpeg and SDL2
Dense matching library based on PyTorch
Productive, portable, and performant GPU programming in Python.
Unofficial implementation of ZipNeRF
PyTorch code and models for the DINOv2 self-supervised learning method.
A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF
Official implementation for the paper "Deep ViT Features as Dense Visual Descriptors".
All NeRF-related papers @ CVPR/ICCV/ECCV/NIPS/ICML/ICLR
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.
Instant-ngp in pytorch+cuda trained with pytorch-lightning (high quality with high speed, with only few lines of legible code)