11 accepted papers
While LiDAR NeRFs excel in static environments, synthesizing dynamic scenes remains challenging as moving objects break multi-view consistency, causing conflicting supervision and ghosting artifacts across frames. Existing methods typically suffer from optimization difficulty from the start, struggl…
In this paper, we aim to create physical digital twins of deformable objects under interaction. Existing methods focus more on the physical learning of current state modeling, but generalize worse to future prediction. This is because existing methods ignore the intrinsic physical properties of defo
Prevailing scene coordinate regression methods for LiDAR localization suffer from localization ambiguities, as distinct locations can exhibit similar geometric signatures — a challenge that current geometry-based regression approaches have yet to solve. Recent vision–language models show that textua…
Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long training times make these impractical for applications requiring time-sensitive system upgrades, such as autonomous drivin…
Place recognition (PR) aims at retrieving the query place from a database and plays a crucial role in various applications, including navigation, autonomous driving, and augmented reality. While previous multi-modal PR works have mainly focused on the same-view scenario in which ground-view descript…
LiDAR localization is a fundamental task in autonomous driving and robotics. Scene Coordinate Regression (SCR) exhibits leading pose accuracy, achieving impressive results in learning-based localization. We observe that the real-world LiDAR scans captured from different viewpoints usually result in…
While Neural Radiance Fields (NeRFs) have advanced the frontiers of novel view synthesis (NVS) using LiDAR data, they still struggle in dynamic scenes. Due to the low frequency and sparsity characteristics of LiDAR point clouds, it is challenging to spontaneously learn a dynamic and consistent scene…
Multi-modal perception is essential for unmanned aerial vehicle (UAV) operations, as it enables a comprehensive understanding of the UAVs' surrounding environment. However, most existing multi-modal UAV datasets are primarily biased toward localization and 3D reconstruction tasks, or only support ma…
Absolute pose regression (APR) estimates global pose in an end-to-end manner achieving impressive results in learn-based LiDAR localization. However compared to the top-performing methods reliant on 3D-3D correspondence matching APR's accuracy still has room for improvement. We recognize APR's lack…
"Despite the significant advancements in pre-training methods for point cloud understanding, directly capturing intricate shape information from irregular point clouds without reliance on external data remains a formidable challenge. To address this problem, we propose GPSFormer, an innovative Globa…
LiDAR-based absolute pose regression estimates the global pose through a deep network in an end-to-end manner, achieving impressive results in learning-based localization. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding the scene geom…