Recent query-based 3D object detection methods using camera and LiDAR inputs have shown strong performance, but existing query initialization strategies,such as random sampling or BEV heatmap-based sampling, often result in inefficient query usage and reduced accuracy, particularly for occluded or crowded objects. To address this limitation, we propose ALIGN (Advanced query initialization with LiDAR and Image GuidaNce), a novel approach for occlusion-robust, object-aware query initialization. Our model consists of three key components: (i) Occlusion-aware Center Estimation (OCE), which integrates LiDAR geometry and image semantics to estimate object centers accurately (ii) Adaptive Neighbor Sampling (ANS), which generates object candidates from LiDAR clustering and supplements each object by sampling spatially and semantically aligned points around it and (iii) Dynamic Query Balancing (DQB), which adaptively balances queries between foreground and background regions. Our extensive experiments on the nuScenes benchmark demonstrate that ALIGN consistently improves performance across multiple state-of-the-art detectors, achieving gains of up to +0.9 mAP and +1.2 NDS, particularly in challenging scenes with occlusions or dense crowds. Our code will be publicly available upon publication.

翻译：近期基于查询的、使用相机与激光雷达输入的三维目标检测方法已展现出强劲性能，但现有的查询初始化策略，如随机采样或基于BEV热图的采样，常导致查询使用效率低下和精度下降，尤其对于被遮挡或密集物体。为应对此局限，我们提出ALIGN（基于激光雷达与图像引导的先进查询初始化），一种面向遮挡鲁棒性、物体感知查询初始化的新方法。我们的模型包含三个关键组件：(i) 遮挡感知中心估计（OCE），其融合激光雷达几何信息与图像语义以精确估计物体中心；(ii) 自适应邻域采样（ANS），其从激光雷达聚类生成物体候选，并通过在物体周围采样空间与语义对齐的点进行补充；(iii) 动态查询平衡（DQB），其自适应地平衡前景与背景区域间的查询。我们在nuScenes基准测试上的大量实验表明，ALIGN能持续提升多种先进检测器的性能，实现高达+0.9 mAP与+1.2 NDS的性能增益，尤其在存在遮挡或密集人群的挑战性场景中。我们的代码将在论文发表后公开。