TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes
Journal:
arXiv
Published Date:
Dec 13, 2024
Abstract
We tackle the problem of localizing traffic cameras within a 3D reference map
and propose a novel image-to-point cloud registration (I2P) method, TrafficLoc,
in a coarse-tofine matching fashion. To overcome the lack of large-scale
real-world intersection datasets, we first introduce Carla Intersection, a new
simulated dataset with 75 urban and rural intersections in Carla. We find that
current I2P methods struggle with cross-modal matching under large viewpoint
differences, especially at traffic intersections. TrafficLoc thus employs a
novel Geometry-guided Attention Loss (GAL) to focus only on the corresponding
geometric regions under different viewpoints during 2D-3D feature fusion. To
address feature inconsistency in paired image patch-point groups, we further
propose Inter-intra Contrastive Learning (ICL) to enhance separating 2D
patch/3D group features within each intra-modality and introduce Dense Training
Alignment (DTA) with soft-argmax for improving position regression. Extensive
experiments show our TrafficLoc greatly improves the performance over the SOTA
I2P methods (up to 86%) on Carla Intersection and generalizes well to
real-world data. TrafficLoc also achieves new SOTA performance on KITTI and
NuScenes datasets, demonstrating the superiority across both in-vehicle and
traffic cameras. Our project page is publicly available at
https://tum-luk.github.io/projects/trafficloc/.
