116 research outputs found
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
We study the problem of extracting accurate correspondences for point cloud
registration. Recent keypoint-free methods have shown great potential through
bypassing the detection of repeatable keypoints which is difficult to do
especially in low-overlap scenarios. They seek correspondences over downsampled
superpoints, which are then propagated to dense points. Superpoints are matched
based on whether their neighboring patches overlap. Such sparse and loose
matching requires contextual features capturing the geometric structure of the
point clouds. We propose Geometric Transformer, or GeoTransformer for short, to
learn geometric feature for robust superpoint matching. It encodes pair-wise
distances and triplet-wise angles, making it invariant to rigid transformation
and robust in low-overlap cases. The simplistic design attains surprisingly
high matching accuracy such that no RANSAC is required in the estimation of
alignment transformation, leading to times acceleration. Extensive
experiments on rich benchmarks encompassing indoor, outdoor, synthetic,
multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our
method improves the inlier ratio by percentage points and the
registration recall by over points on the challenging 3DLoMatch benchmark.
Our code and models are available at
\url{https://github.com/qinzheng93/GeoTransformer}.Comment: Accepted by TPAMI. Extended version of our CVPR 2022 paper
[arXiv:2202.06688
Visual Map Construction Using RGB-D Sensors for Image-Based Localization in Indoor Environments
RGB-D sensors capture RGB images and depth images simultaneously, which makes it possible to acquire the depth information at pixel level. This paper focuses on the use of RGB-D sensors to construct a visual map which is an extended dense 3D map containing essential elements for image-based localization, such as poses of the database camera, visual features, and 3D structures of the building. Taking advantage of matched visual features and corresponding depth values, a novel local optimization algorithm is proposed to achieve point cloud registration and database camera pose estimation. Next, graph-based optimization is used to obtain the global consistency of the map. On the basis of the visual map, the image-based localization method is investigated, making use of the epipolar constraint. The performance of the visual map construction and the image-based localization are evaluated on typical indoor scenes. The simulation results show that the average position errors of the database camera and the query camera can be limited to within 0.2 meters and 0.9 meters, respectively
Digital 3D Technologies for Humanities Research and Education: An Overview
Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods
- …