RECO：用于路边3D检测中外在扰动的区域感知补偿

中文摘要 AI 辅助

在智能交通系统中，路边3D目标检测对交通理解、协同预警和安全自动驾驶至关重要。但现有方法对相机外部参数高度敏感，微小偏差会因投影几何被显著放大，导致特征严重错位和定位退化。为此提出RECO，一种区域感知外部补偿框架，用分段6自由度姿态偏移校正外部参数。它预测可学习范围边界划分场景为远近区域，估计特定区域姿态校正。通过可微Sigmoid门平滑融合两种补偿几何以保持连续BEV采样并促进稳定优化。引入辅助重投影损失监督外部参数细化，将3D地面真值投影的2D边界框与2D注释比较，与标准检测目标联合优化。在DAIR-V2X-I和Rope3D基准上的大量实验表明，在偏航和z轴偏差方面均优于现有基线，且能从瞬态扰动推广到持续偏移，在严格校准不确定性下保持高竞争力性能。

英文摘要

In intelligent transportation systems, roadside 3D object detection provides wide-area perception crucial for traffic understanding, cooperative early warning, and safe autonomous driving. However, existing methods suffer from high sensitivity to camera extrinsics; even slight deviations (whether manifesting as transient jitter or persistent drift) can be significantly amplified by projective geometry. This cascade results in severe feature misalignment and degraded localization. To mitigate this limitation, we propose RECO, a region-aware extrinsic compensation framework that corrects extrinsics using piecewise 6-DoF pose offsets. RECO predicts a learnable range boundary to partition the scene into near and far regions, estimating region-specific pose corrections. A differentiable sigmoid gate then smoothly blends the two compensated geometries to preserve continuous BEV sampling and facilitate stable optimization. To supervise the refinement of extrinsics, we introduce an auxiliary reprojection loss that compares 2D bounding boxes projected from 3D ground truth against 2D annotations, optimizing it jointly with the standard detection objective. Extensive experiments on the DAIR-V2X-I and Rope3D benchmarks under extrinsic perturbations demonstrate consistent improvements over state-of-the-art baselines across both yaw and $z$-axis deviations. RECO also generalizes from transient perturbations to persistent shifts, maintaining highly competitive performance under strict calibration uncertainty.

发表机构

• School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
• Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）

机构由 AI 辅助整理，请以论文原文为准。