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[SIGGRAPH Asia 2024] Streaming Volumetric Video SIBR Viewer for V3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians
VUDA is a header-only library based on Vulkan that provides a CUDA Runtime API interface for writing GPU-accelerated applications.
Three.js-based implementation of 3D Gaussian splatting
Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
A cross-platform, high performance renderer for Gaussian Splatting using Vulkan Compute. Supports ✅ Windows, Linux, macOS, iOS, and visionOS
🌭🌭🌭 Gaussian splatting render by Vulkan-ComputerShader, supports running in any environment(AMD,Mac,Android...)
Vulkan-based Gaussian Splatting viewer, and python binding
Full python interactive 3D Gaussian Splatting viewer for real-time editing and analyzing.
A VR viewer for gaussian splatting models developped as native plugin for unity with the original CUDA rasterizer.
A 3D Gaussian Splatting framework with various derived algorithms and an interactive web viewer
3D Model Viewer supporting glTF and 3D Gaussian Splats
Navicat Premium 15 破解激活(亲测有效)如果经济允许,还是希望可以支持正版!!!windows10 & ubuntu
Official implementation of the paper "Gaussian in the Wild: 3D Gaussian Splatting for Unconstrained Image Collections" [ECCV 2024]
Lightweight Armoury Crate alternative for Asus laptops and ROG Ally. Control tool for ROG Zephyrus G14, G15, G16, M16, Flow X13, Flow X16, TUF, Strix, Scar and other models
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
[ICLR 2023 Oral] Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
[ICML 2024] Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation
[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation
[CVPR 2023] 3D Cinemagraphy from a Single Image
Painting 3D Nature in 2D: View Synthesis of Natural Scenes From a Single Semantic Mask