Schedule-Free Optimization in PyTorch

A fast and memory-efficient libarary for sparse transformer with varying token numbers (e.g., 3D point cloud).

The repo for "Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image" and "Metric3Dv2: A Versatile Monocular Geometric Foundation Model..."

RaDe-GS: Rasterizing Depth in Gaussian Splatting

A python parametric CAD scripting framework based on OCCT

MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment

code for "PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction"

Code Release for "Bilateral Guided Radiance Field Processing"

Intrinsic Image Diffusion for Single-view Material Estimation

[SIGGRAPH2024] DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models

SECOND for KITTI/NuScenes object detection

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.

SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM (CVPR 2024)

[CVPR'24 Highlight] Gaussian Splatting SLAM

Official Implementation of Neural Splines

Point Cloud Library (PCL)

Correlation Clustering on weighted mesh under an anisotropic metric

raster grid as indexed quad mesh

Lightplane implements a highly memory-efficient differentiable radiance field renderer, and a module for unprojecting features from images to 3D grids.

Kolmogorov Arnold Networks

Code for "FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent" by Cameron Smith*, David Charatan*, Ayush Tewari, and Vincent Sitzmann

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

ACE0 is a learning-based structure-from-motion approach that estimates camera parameters of sets of images by learning a multi-view consistent, implicit scene representation.

LightGlue: Local Feature Matching at Light Speed (ICCV 2023)

[CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"