Advances in the scientific fields of photogrammetry and computer vision have led to the
development of automated multi-image methods that solve the problem of 3D reconstruction …

A point cloud as a collection of points is poised to bring about a revolution in acquiring and
generating three‐dimensional (3D) surface information of an object in 3D reconstruction …

Abstract We present Block-NeRF, a variant of Neural Radiance Fields that can represent
large-scale environments. Specifically, we demonstrate that when scaling NeRF to render …

Purely MLP-based neural radiance fields (NeRF-based methods) often suffer from
underfitting with blurred renderings on large-scale scenes due to limited model capacity …

Existing NeRF-based methods for large scene reconstruction often have limitations in visual
quality and rendering speed. While the recent 3D Gaussian Splatting works well on small …

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D
cost volumes to regularize and regress the output depth or disparity. These methods are …

We introduce a comprehensive benchmark for local features and robust estimation
algorithms, focusing on the downstream task—the accuracy of the reconstructed camera …

Image-guided depth completion aims to generate dense depth maps with sparse depth
measurements and corresponding RGB images. Currently, spatial propagation networks …

Most existing studies on learning local features focus on the patch-based descriptions of
individual keypoints, whereas neglecting the spatial relations established from their keypoint …

We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
Registration of multiple scans typically follows a two-stage pipeline: the initial pairwise …