论文标题
CL3D：跨LIDAR 3D检测的无监督域适应
CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection
论文作者
论文摘要
跨LIDAR 3D检测的域适应性由于在原始数据表示方面存在较大的差距，并具有不同的点密度和点排列。通过探索域不变的3D几何特性和运动模式，我们提出了一种无监督的域适应方法，该方法克服了上面的困难。首先，我们建议空间几何比对模块提取同一对象类的相似3D形状几何特征以对齐两个域，同时消除了不同点分布的效果。其次，我们提出时间运动比对模块，以利用数据顺序的数据帧中的运动特征匹配两个域。由两个模块产生的原型纳入了伪标签重新释放程序中，并有助于我们对目标域的有效自我训练框架。广泛的实验表明，我们的方法在跨设备数据集上实现了最先进的性能，尤其是对于在各种场景中通过机械扫描激光雷达和固态激光雷达捕获的较大间隙的数据集。 Project Homepage在https://github.com/4dvlab/cl3d.git上
Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on the raw data representation with disparate point densities and point arrangements. By exploring domain-invariant 3D geometric characteristics and motion patterns, we present an unsupervised domain adaptation method that overcomes above difficulties. First, we propose the Spatial Geometry Alignment module to extract similar 3D shape geometric features of the same object class to align two domains, while eliminating the effect of distinct point distributions. Second, we present Temporal Motion Alignment module to utilize motion features in sequential frames of data to match two domains. Prototypes generated from two modules are incorporated into the pseudo-label reweighting procedure and contribute to our effective self-training framework for the target domain. Extensive experiments show that our method achieves state-of-the-art performance on cross-device datasets, especially for the datasets with large gaps captured by mechanical scanning LiDARs and solid-state LiDARs in various scenes. Project homepage is at https://github.com/4DVLab/CL3D.git
