19,190 research outputs found
Stress-Testing LiDAR Registration
Point cloud registration (PCR) is an important task in many fields including
autonomous driving with LiDAR sensors. PCR algorithms have improved
significantly in recent years, by combining deep-learned features with robust
estimation methods. These algorithms succeed in scenarios such as indoor scenes
and object models registration. However, testing in the automotive LiDAR
setting, which presents its own challenges, has been limited. The standard
benchmark for this setting, KITTI-10m, has essentially been saturated by recent
algorithms: many of them achieve near-perfect recall.
In this work, we stress-test recent PCR techniques with LiDAR data. We
propose a method for selecting balanced registration sets, which are
challenging sets of frame-pairs from LiDAR datasets. They contain a balanced
representation of the different relative motions that appear in a dataset, i.e.
small and large rotations, small and large offsets in space and time, and
various combinations of these.
We perform a thorough comparison of accuracy and run-time on these
benchmarks. Perhaps unexpectedly, we find that the fastest and simultaneously
most accurate approach is a version of advanced RANSAC. We further improve
results with a novel pre-filtering method
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201
- …