Multi-view stereo infers the 3D geometry from a set of images captured from several known
positions and viewpoints. It is one of the most important components of 3D reconstruction …

Traditionally, 3D indoor scene reconstruction from posed images happens in two phases:
per-image depth estimation, followed by depth merging and surface reconstruction …

The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired
researchers to propose neural implicit scene reconstruction. However, most existing neural …

Abstract Empowering 3D Gaussian Splatting with generalization ability is appealing.
However, existing generalizable 3D Gaussian Splatting methods are largely confined to …

Multi-frame depth estimation generally achieves high accuracy relying on the multi-view
geometric consistency. When applied in dynamic scenes, eg, autonomous driving, this …

Recent volumetric 3D reconstruction methods can produce very accurate results, with
plausible geometry even for unobserved surfaces. However, they face an undesirable trade …

Recent works on 3D reconstruction from posed images have demonstrated that direct
inference of scene-level 3D geometry without test-time optimization is feasible using deep …

3D reconstruction aims to recover the dense 3D structure of a scene. It plays an essential
role in various applications such as Augmented/Virtual Reality (AR/VR), autonomous driving …

To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo
techniques rely on two distinct stages: local depth maps computation and global depth maps …

Gaussian splatting and single/multi-view depth estimation are typically studied in isolation.
In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation …