SparseGS
SparseGS augments 3D Gaussian Splatting with depth-based priors, tailored depth rendering, a floater-pruning heuristic, and Unseen Viewpoint Regularization, letting it overcome “floaters” and background collapse when training views are scarce. Tested on Mip-NeRF360, LLFF, and DTU, it still trains quickly and renders in real time while reconstructing unbounded or forward-facing scenes from as few as 12 and 3 input images, respectively.
Web: https://formycat.github.io/SparseGS-Real-Time-360-Sparse-View-Synthesis-using-Gaussian-Splatting/
Authors: Haolin Xiong, Sairisheek Muttukuru, Rishi Upadhyay, Pradyumna Chari, Achuta Kadambi
Mip-NeRF 360 Sparse
Modified Mip-NeRF 360 dataset with small train set (12 or 24) views. The dataset is used to evaluate sparse-view NVS methods.