Bibliographic Details
| Title: |
DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment |
| Authors: |
Liu, Jiuming, Zhuo, Dong, Feng, Zhiheng, Zhu, Siting, Peng, Chensheng, Liu, Zhe, Wang, Hesheng |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
Information inside visual and LiDAR data is well complementary derived from the fine-grained texture of images and massive geometric information in point clouds. However, it remains challenging to explore effective visual-LiDAR fusion, mainly due to the intrinsic data structure inconsistency between two modalities: Image pixels are regular and dense, but LiDAR points are unordered and sparse. To address the problem, we propose a local-to-global fusion network (DVLO) with bi-directional structure alignment. To obtain locally fused features, we project points onto the image plane as cluster centers and cluster image pixels around each center. Image pixels are pre-organized as pseudo points for image-to-point structure alignment. Then, we convert points to pseudo images by cylindrical projection (point-to-image structure alignment) and perform adaptive global feature fusion between point features and local fused features. Our method achieves state-of-the-art performance on KITTI odometry and FlyingThings3D scene flow datasets compared to both single-modal and multi-modal methods. Codes are released at https://github.com/IRMVLab/DVLO.
Comment: Accepted by ECCV 2024.Codes are released at https://github.com/IRMVLab/DVLO |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2403.18274 |
| Accession Number: |
edsarx.2403.18274 |
| Database: |
arXiv |
