Neural surface reconstruction has been shown to be powerful for recovering dense 3D
surfaces via image-based neural rendering. However, current methods struggle to recover …

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction,
achieving high quality novel view synthesis and fast rendering speed. However, 3DGS fails …

Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields
(NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as …

We present ScanNet++, a large-scale dataset that couples together capture of high-quality
and commodity-level geometry and color of indoor scenes. Each scene is captured with a …

Abstract Neural Radiance Field (NeRF) significantly degrades when only a limited number
of views are available. To complement the lack of 3D information, depth-based models, such …

Abstract 3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at
rendering photorealistic novel views of complex scenes. However recovering a high-quality …

Recent methods for neural surface representation and rendering, for example NeuS, have
demonstrated the remarkably high-quality reconstruction of static scenes. However, the …

We introduce MVSplat, an efficient model that, given sparse multi-view images as input,
predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we …