Projects sharing features with Pointnerf

Matthew Tancik\ 1 , Ben Mildenhall\ 1 , Terrance Wang 1 , Divi Schmidt 1 , Pratul P. Srinivasan 2 , Jonathan T. Barron 2 , Ren Ng 1

Code and models for our ICCV 2021 paper "MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis"

ECCV 2022 "SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

CVPR 2024 Official code release for "UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable Sets"

Pytorch code for ICCV'23 paper. NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes

CVPR'24 Best Student Paper Mip-Splatting: Alias-free 3D Gaussian Splatting

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

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

PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021.

SIGGRAPH'24 2D Gaussian Splatting for Geometrically Accurate Radiance Fields