The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://github.com/mystorm16/BSH-Det3D.

翻译：基于激光雷达的3D目标检测技术的进步显著推动了自动驾驶与机器人领域的发展。然而，受限于激光雷达传感器的固有局限，被遮挡区域及远距离区域的物体形状会出现退化，这给3D感知带来了根本性挑战。现有方法通过估计特定3D形状取得了显著性能，但这些方法依赖大量计算与内存资源，导致精度与实时性失衡。为解决这一挑战，我们提出一种名为BSH-Det3D的新型基于激光雷达的3D目标检测模型，该模型采用从鸟瞰图（BEV）估计完整形状的有效方式来增强空间特征。具体而言，我们设计了基于柱体的形状补全（PSC）模块，用于预测柱体是否包含物体形状的占据概率。该PSC模块为每个场景生成BEV形状热力图。在与热力图融合后，BSH-Det3D能够在形状退化区域提供额外信息，并生成高质量3D候选框。我们还设计了一种基于注意力的稠密化融合模块（ADF），用以自适应地将稀疏特征与热力图及原始点云相关联。该ADF模块以可忽略的额外开销整合了点云与形状知识的优势。在KITTI基准上的大量实验表明，BSH-Det3D在精度与速度方面均达到了最先进（SOTA）性能，验证了其高效性与灵活性。源代码已开源至https://github.com/mystorm16/BSH-Det3D。