Diffgs: Functional gaussian splatting diffusion
Abstract 3D Gaussian Splatting (3DGS) has shown convincing performance in rendering
speed and fidelity, yet the generation of Gaussian Splatting remains a challenge due to its …
speed and fidelity, yet the generation of Gaussian Splatting remains a challenge due to its …
Learning unsigned distance functions from multi-view images with volume rendering priors
Unsigned distance functions (UDFs) have been a vital representation for open surfaces.
With different differentiable renderers, current methods are able to train neural networks to …
With different differentiable renderers, current methods are able to train neural networks to …
Neural signed distance function inference through splatting 3d gaussians pulled on zero-level set
It is vital to infer a signed distance function (SDF) in multi-view based surface reconstruction.
3D Gaussian splatting (3DGS) provides a novel perspective for volume rendering, and …
3D Gaussian splatting (3DGS) provides a novel perspective for volume rendering, and …
NeuralTPS: Learning signed distance functions without priors from single sparse point clouds
Surface reconstruction for point clouds is one of the important tasks in 3D computer vision.
The latest methods rely on generalizing the priors learned from large scale supervision …
The latest methods rely on generalizing the priors learned from large scale supervision …
Inferring 3D occupancy fields through implicit reasoning on silhouette images
Implicit 3D representations have shown great promise in deep learning-based 3D
reconstruction. With differentiable renderers, current methods are able to learn implicit …
reconstruction. With differentiable renderers, current methods are able to learn implicit …