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 …

We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

Image Matching is a core component of all best-performing algorithms and pipelines in 3D
vision. Yet despite matching being fundamentally a 3D problem, intrinsically linked to …

Abstract Learning-based Multi-View Stereo (MVS) methods warp source images into the
reference camera frustum to form 3D volumes, which are fused as a cost volume to be …

We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that
reconstructs Neural Radiance Fields (NeRFs) and learns to renders novel views on the fly …

Abstract 3D reconstruction of scenes using multiple images, relying on robust
correspondence search and depth estimation, has been thoroughly studied for the two‐view …

Abstract Recent cascade Multi-View Stereo (MVS) methods can efficiently estimate high-
resolution depth maps through narrowing hypothesis ranges. However, previous methods …

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost
volumes. Due to the high computation and memory consumption of 3D CNN, the resolution …

We present MVSGaussian, a new generalizable 3D Gaussian representation approach
derived from Multi-View Stereo (MVS) that can efficiently reconstruct unseen scenes …

In this paper, we propose a practical three-dimensional (3D) real-scene reconstruction
framework named Deep3D, which is paired with a deep learning based multi-view stereo …