Solution for UCF UrbanTwin V2X-Real Track: Sim-to-Real Urban LiDAR 3D Object Detection

2026-09-07

AI Summary

为解决路边LiDAR从模拟到现实的转换问题，提出了一种多源协作训练与类别感知融合框架，通过整合不同类型的扫描数据来提高3D物体检测精度。

Abstract

Bridging the simulation-to-reality gap in roadside LiDAR requires addressing several coupled discrepancies, including scene geometry, sampling density, return patterns, and pedestrian scale. This report presents a multi-source collaborative training and class-aware fusion framework for Sim2Real 3D detection. The method organizes digital-twin scans, diffusion-redrawn scans, density-stabilized scans, and pedestrian morphology-aligned samples into a unified training pool with complementary roles. Within a common DSVT detection formulation, source-specialized expert branches preserve those roles while optimizing for the same detection objective. At inference, a predefined class-aware fusion pathway integrates geometry-stable and calibration-aware branches for vehicles, sampling-complementary branches for trucks, and morphology-consistent evidence for pedestrians. A label-free point-cloud center blend then refines geometric localization. On the UrbanTwin V2X-Real hidden test set, the unified system achieves a combined score of 0.7421, with 3D mAP@0.5 of 0.4518 and a realism score of 0.8871. The results indicate that a stable, interpretable collaboration among data sources is more valuable than unconstrained aggregation of model outputs.

Problem

• Sim2Real
• LiDAR
• 3D Object Detection
• Simulation-to-Reality Gap
• UrbanTwin V2X-Real

Innovation

• Sim2Real
• multi-source collaborative training
• class-aware fusion
• density-stabilized scans
• pedestrian morphology-aligned