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[CVPR'24 Best Student Paper] Mip-Splatting: Alias-free 3D Gaussian Splatting
The main features of autonomousvision/mip-splatting are: Neural Scene Representation.
Projects with overlapping indexed features include: bmild/nerf — This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of… google-research/multinerf — MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel… gyhandy/neural-sim-nerf. hbb1/2d-gaussian-splatting — [SIGGRAPH'24] 2D Gaussian Splatting for Geometrically Accurate Radiance Fields. linhuang17/ncf-code. nerf2nerf/nerf2nerf.
MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel views from sets of 2D images. It provides a system for generating new perspectives of a scene by optimizing a neural network based on images and camera poses. The toolkit includes research implementations such as Mip-NeRF 360 and Ref-NeRF for high-fidelity volumetric rendering. It features a structure-from-motion pipeline to calculate camera positions and orientations from image datasets to prepare data for training. The project covers a full workflow for volumetric rendering, i
SIGGRAPH'24 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
This project is a framework for neural radiance fields used to synthesize three-dimensional environments from sets of two-dimensional images and camera poses. It functions as a volumetric rendering engine and scene synthesizer that optimizes neural representations of spatial volumes to generate novel views of complex 3D scenes. The system implements a coordinate encoding system that transforms spatial coordinates into high-dimensional space to capture high-frequency geometric details. It also includes a neural mesh extractor that converts trained radiance fields into triangle meshes via march