CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition
- URL: http://arxiv.org/abs/2303.17778v2
- Date: Fri, 29 Sep 2023 22:34:43 GMT
- Title: CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition
- Authors: Tianrui Guan, Aswath Muthuselvam, Montana Hoover, Xijun Wang, Jing
Liang, Adarsh Jagan Sathyamoorthy, Damon Conover, Dinesh Manocha
- Abstract summary: CrossLoc3D is a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting.
We present CS-Campus3D, the first 3D aerial-ground cross-source dataset consisting of point cloud data from both aerial and ground LiDAR scans.
- Score: 45.16530801796705
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present CrossLoc3D, a novel 3D place recognition method that solves a
large-scale point matching problem in a cross-source setting. Cross-source
point cloud data corresponds to point sets captured by depth sensors with
different accuracies or from different distances and perspectives. We address
the challenges in terms of developing 3D place recognition methods that account
for the representation gap between points captured by different sources. Our
method handles cross-source data by utilizing multi-grained features and
selecting convolution kernel sizes that correspond to most prominent features.
Inspired by the diffusion models, our method uses a novel iterative refinement
process that gradually shifts the embedding spaces from different sources to a
single canonical space for better metric learning. In addition, we present
CS-Campus3D, the first 3D aerial-ground cross-source dataset consisting of
point cloud data from both aerial and ground LiDAR scans. The point clouds in
CS-Campus3D have representation gaps and other features like different views,
point densities, and noise patterns. We show that our CrossLoc3D algorithm can
achieve an improvement of 4.74% - 15.37% in terms of the top 1 average recall
on our CS-Campus3D benchmark and achieves performance comparable to
state-of-the-art 3D place recognition method on the Oxford RobotCar. The code
and CS-CAMPUS3D benchmark will be available at github.com/rayguan97/crossloc3d.
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