CornerPoint3D：关注最近的角点而非中心
CornerPoint3D: Look at the Nearest Corner Instead of the Center
AI总结：
针对LiDAR跨域3D目标检测中基于中心检测器定位差的问题，提出关注最近角点的CornerPoint3D检测器、新评估指标及EdgeHead细化头，显著提升跨域鲁棒性。
摘要：
3D目标检测旨在从LiDAR点云中预测物体的中心、尺寸和旋转。尽管LiDAR简单易用，但它仅能捕获物体的近侧，使得基于中心的检测器在点分布变化的跨域任务中容易出现定位精度差的问题。同时，现有的针对单域评估设计的评估指标也会因数据集特定的尺寸变化而面临过拟合问题。一个关键问题随之而来：我们真的需要模型在跨域应用后，在整个3D边界框上保持出色的性能吗？实际上，我们的主要关注点之一是防止车辆与其他障碍物发生碰撞，尤其是在正确预测尺寸困难得多的跨域场景中。为了解决这些问题，我们从实际角度重新思考跨域3D目标检测。我们提出了两个新指标，用于评估模型检测物体更靠近LiDAR传感器表面的能力。此外，我们引入了EdgeHead，这是一个细化头，引导模型更关注可学习的较近表面，在我们新的和传统的BEV/3D指标下都显著提升了跨域性能。进一步地，我们认为预测最近的角点而非物体中心能增强鲁棒性。我们提出了一种名为CornerPoint3D的新型3D目标检测器，它基于CenterPoint构建，并使用热图来监督每个物体最近角点的学习和检测。我们提出的方法在整个边界框的检测质量与LiDAR传感器较近表面的定位精度之间实现了平衡的权衡，在多个跨域任务中优于传统的基于中心的检测器CenterPoint，并提供了一个更具实际合理性和鲁棒性的跨域3D目标检测解决方案。
英文摘要：
3D object detection aims to predict object centers, dimensions, and rotations from LiDAR point clouds. Despite its simplicity, LiDAR captures only the near side of objects, making center-based detectors prone to poor localization accuracy in cross-domain tasks with varying point distributions. Meanwhile, existing evaluation metrics designed for single-domain assessment also suffer from overfitting due to dataset-specific size variations. A key question arises: Do we really need models to maintain excellent performance in the entire 3D bounding boxes after being applied across domains? Actually, one of our main focuses is on preventing collisions between vehicles and other obstacles, especially in cross-domain scenarios where correctly predicting the sizes is much more difficult. To address these issues, we rethink cross-domain 3D object detection from a practical perspective. We propose two new metrics that evaluate a model's ability to detect objects' closer-surfaces to the LiDAR sensor. Additionally, we introduce EdgeHead, a refinement head that guides models to focus more on learnable closer surfaces, significantly improving cross-domain performance under both our new and traditional BEV/3D metrics. Furthermore, we argue that predicting the nearest corner rather than the object center enhances robustness. We propose a novel 3D object detector, coined as CornerPoint3D, which is built upon CenterPoint and uses heatmaps to supervise the learning and detection of the nearest corner of each object. Our proposed methods realize a balanced trade-off between the detection quality of entire bounding boxes and the locating accuracy of closer surfaces to the LiDAR sensor, outperforming the traditional center-based detector CenterPoint in multiple cross-domain tasks and providing a more practically reasonable and robust cross-domain 3D object detection solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
