先对齐再融合：面向鲁棒3D检测的2D引导跨模态对齐
Look Before You Fuse: 2D-Guided Cross-Modal Alignment for Robust 3D Detection
用2D检测器可靠定位的物体-背景边界作为先验，在融合前预对齐激光雷达与相机特征，并显式增强边界处的深度突变；这套「先对齐再融合」的思路可直接迁移到任何存在投影误差的多传感器BEV融合流程中。
原文依据 utilize 2D object priors to pre-align cross-modal features before fusion
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论将激光雷达与相机输入整合进统一的鸟瞰图（BEV）表示，对提升自动驾驶车辆的三维感知能力至关重要。然而，现有方法存在激光雷达与相机特征之间的空间错位问题，这会导致相机分支中的深度监督不准确，并在跨模态特征聚合时产生错误的融合。该错位的根本原因在于投影误差，其来源于标定不准确与卷帘快门效应。本文的关键洞见是：这些投影误差的位置并非随机，而是高度可预测的，因为它们集中在物体与背景的边界处，而2D检测器能够可靠地识别这些边界。基于此，我们的主要动机是在融合之前利用2D物体先验对跨模态特征进行预对齐。针对局部错位，我们提出先验引导深度校准（PGDC），利用2D先验缓解错位并保留正确的跨模态特征对。针对全局错位，我们引入不连续性感知几何融合（DAGF），以抑制PGDC残留的噪声，并显式增强物体-背景边界处的深度突变，从而得到结构感知的表示。为有效利用这些已对齐的表示，我们引入结构引导深度调制器（SGDM），使用门控注意力机制高效融合对齐后的深度特征与图像特征。本方法在nuScenes验证集上取得SOTA性能，平均精度均值（mAP）与NDS分别达到71.5%与73.6%。此外，在Argoverse 2验证集上，我们取得了41.7%的具有竞争力的mAP。
英文原文摘要 1684 字符
Integrating LiDAR and camera inputs into a unified Bird's-Eye-View (BEV) representation is crucial for enhancing 3D perception capabilities of autonomous vehicles. However, existing methods suffer from spatial misalignment between LiDAR and camera features, which causes inaccurate depth supervision in camera branch and erroneous fusion during cross-modal feature aggregation. The root cause of this misalignment lies in projection errors, stemming from calibration inaccuracies and rolling shutter effect.The key insight of this work is that locations of these projection errors are not random but highly predictable, as they are concentrated at object-background boundaries which 2D detectors can reliably identify. Based on this, our main motivation is to utilize 2D object priors to pre-align cross-modal features before fusion. To address local misalignment, we propose Prior Guided Depth Calibration (PGDC), which leverages 2D priors to alleviate misalignment and preserve correct cross-modal feature pairs. To resolve global misalignment, we introduce Discontinuity Aware Geometric Fusion (DAGF) to suppress residual noise from PGDC and explicitly enhance sharp depth transitions at object-background boundaries, yielding a structurally aware representation. To effectively utilize these aligned representations, we incorporate Structural Guidance Depth Modulator (SGDM), using a gated attention mechanism to efficiently fuse aligned depth and image features. Our method achieves SOTA performance on nuScenes validation dataset, with its mAP and NDS reaching 71.5% and 73.6% respectively. Additionally, on the Argoverse 2 validation set, we achieve a competitive mAP of 41.7%.