Name
Abstract | ||
|---|---|---|
This paper presents an approach for learning-based discriminative 3D point cloud descriptor from RGB-D images for place recognition purposes in indoor environments. Existing methods, such as such as Point-NetVLAD, PCAN or LPD-Net, are aimed at outdoor environments and operate on 3D point clouds from LiDAR. They are based on PointNet architecture and designed to process only the scene geometry and do not consider appearance (RGB component). In this paper we present a place recognition method based on sparse volumetric representation and processing scene appearance in addition to the geometry. We also investigate if using two modalities, appearance (RGB data) and geometry (3D structure), improves discriminativity of a resultant global descriptor. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.5220/0010340502160224 | VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP |
Keywords | DocType | Citations |
Place Recognition, 3D Point Cloud, RGB-D, Deep Metric Learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacek Komorowski | 1 | 4 | 4.13 |
| grzegorz kurzejamski | 2 | 3 | 3.10 |
| Monika Wysoczańska | 3 | 0 | 0.34 |
| Tomasz Trzcinski | 4 | 0 | 4.39 |