Papers › HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion
HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion
Sijie Wang, Qiyu Kang, Rui She, Wei Wang, Kai Zhao, Yang song, Wee Peng Tay
LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high computation storage costs and can lead to globally inaccurate pose estimations if the database is too sparse. On the other hand, pose regression methods take images or point clouds as inputs and directly regress global poses in an end-to-end manner. They do not perform database matching and are more computationally efficient than retrieval techniques. We propose HypLiLoc, a new model for LiDAR pose regression. We use two branched backbones to extract 3D features and 2D projection features, respectively. We consider multi-modal feature fusion in both Euclidean and hyperbolic spaces to obtain more effective feature representations. Experimental results indicate that HypLiLoc achieves state-of-the-art performance in both outdoor and indoor datasets. We also conduct extensive ablation studies on the framework design, which demonstrate the effectiveness of multi-modal feature extraction and multi-space embedding. Our code is released at: https://github.com/sijieaaa/HypLiLoc
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Localization | Oxford Radar RobotCar (Full-6) | HypLiLoc | Mean Translation Error | 6.00 | #5 of 16 | Archive leaderboard | report |
| lidar absolute pose regression | Oxford Radar RobotCar (Full-6) | HypLiLoc | Mean Translation/Rotation Error (m/degree) | 6.00 / 1.31 | #1 of 1 | Archive leaderboard | report |
| lidar absolute pose regression | Oxford Radar RobotCar (Full-7) | HypLiLoc | Mean Translation/Rotation Error (m/degree) | 6.88 / 1.09 | #1 of 1 | Archive leaderboard | report |
| lidar absolute pose regression | Oxford Radar RobotCar (Full-8) | HypLiLoc | Mean Translation/Rotation Error (m/degree) | 5.82 / 0.97 | #1 of 1 | Archive leaderboard | report |
| lidar absolute pose regression | Oxford Radar RobotCar (Full-9) | HypLiLoc | Mean Translation/Rotation Error (m/degree) | 3.45 / 0.84 | #1 of 1 | Archive leaderboard | report |
| lidar absolute pose regression | vReLoc (Seq-05) | HypLiLoc | Median Translation/Rotation Error (m/degree) | 0.09 / 2.52 | #1 of 1 | Archive leaderboard | report |
| lidar absolute pose regression | vReLoc (Seq-06) | HypLiLoc | Median Translation/Rotation Error (m/degree) | 0.08 / 2.58 | #1 of 1 | Archive leaderboard | report |
| lidar absolute pose regression | vReLoc (Seq-07) | HypLiLoc | Median Translation/Rotation Error (m/degree) | 0.13 / 2.55 | #1 of 1 | Archive leaderboard | report |
| lidar absolute pose regression | vReLoc (Seq-14) | HypLiLoc | Median Translation/Rotation Error (m/degree) | 0.09 / 2.34 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections