.png){kind=icon}
.png){kind=icon}
Return
PCSU: A Point Cloud Structure Unification Method for Enhancing Generalization in 3-D Object Detection Models
.png){kind=icon}
OA
AI
DOI：10.1109/ojits.2026.3715413.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cross-domain generalization remains a critical challenge in LiDAR-based 3D object detection, primarily due to significant variations in point cloud distributions caused by different sensor configurations, installation angles, and environmental conditions. While prior research has focused extensively on network-level adaptations, the role of point cloud preprocessing has received limited attention. In this paper, we present a systematic study on how structural differences in raw point clouds affect the generalization performance of 3D detection models, and we propose a Point Cloud Structure Unification (PCSU) framework to mitigate these disparities. PCSU comprises three modular components: height normalization to align vertical distributions, reflectivity suppression to reduce overfitting to sensor-specific intensity patterns, and resolution standardization via voxel center alignment. Through extensive experiments across multiple datasets and detection models, we demonstrate that these structure-aware preprocessing strategies generally enhance cross-domain generalization without modifying model architectures. The improvements are most prominent for larger objects and near-to-moderate ranges. Our findings highlight the often-overlooked importance of data-level harmonization and provide actionable insights for designing more generalizable 3D detection pipelines.
Keywords:
LiDAR
3-D object detection
point cloud preprocessing
domain adaptation
structure unification
Journal
I
IF:
5.3
Papers:
184
Citations:
970