面向实时且可自适应的LiDAR场景补全

摘要

激光雷达场景补全是自动驾驶中三维感知的关键组成部分，场景必须实时补全才能用于下游任务。现有方法通常遵循“初始化-细化”范式，即首先构建场景的粗初始化，再细化为完整的三维几何。生成模型速度较慢，因为它们需将随机高斯噪声迭代细化为场景；而非生成方法则以固定噪声尺度扰动部分场景，这限制了对大间隙和遮挡区域的覆盖，并且针对每种新传感器配置都需要手动重新校准。我们提出RapidLiDAR，一种将初始化本身作为学习得到的、数据驱动组件的激光雷达场景补全方法。我们提出自适应初始化模块，为每个部分输入点预测空间变化的位移，将部分观测扩展为适应局部几何的粗场景初始化，无需手动调整噪声。为了将该粗初始化细化为完整且连贯的场景，我们进一步提出多尺度重建模块，通过查询由输入扫描构建的多尺度三维体素和二维鸟瞰图（BEV）特征图来进一步细化点位置。通过用基于体素和BEV的特征提取替代最远点采样和K近邻搜索等点邻域算子，我们的架构速度更快，并且从设计上即可处理不同的输入分辨率。在SemanticKITTI和KITTI-360上的实验表明，我们的方法在补全性能上与最先进方法持平，同时可在0.1秒内完成整个场景，比此前最快方法快2.3倍。这与典型车载激光雷达传感器10 Hz的采集频率相匹配，向实时激光雷达场景补全迈出了一步。

English

LiDAR scene completion is a key component of 3D perception in autonomous driving, where the scene must be completed in real time to be usable in downstream tasks. Existing approaches typically follow an initialize-and-refine paradigm, in which a coarse initialization of the scene is first constructed, then refined into complete 3D geometry. Generative models are slower because they iteratively refine random Gaussian noise into the scene, while non-generative methods perturb the partial scene with a fixed noise scale, which limits coverage of large gaps and occluded regions and requires manual recalibration for each new sensor configuration. We present RapidLiDAR, a LiDAR scene completion method that treats the initialization itself as a learned, data-driven component. We propose an adaptive initialization module that predicts a spatially varying displacement for each partial input point, expanding the partial observations into a coarse scene initialization adapted to the local geometry, without requiring manual noise tuning. To refine this coarse initialization into a complete and coherent scene, we additionally propose a multi-scale reconstruction module that further refines point positions by querying multi-scale 3D voxel and 2D BEV feature maps constructed from the input scan. By replacing point-neighborhood operators such as farthest point sampling and k-nearest neighbor search with voxel- and BEV-based feature extraction, our architecture is faster and can handle different input resolutions by design. Experiments on SemanticKITTI and KITTI-360 show that our method achieves completion performance on par with the state of the art while completing a full scene in 0.1 seconds, which is 2.3 times faster than the fastest prior method. This matches the 10 Hz acquisition rate of typical automotive LiDAR sensors, taking a step toward real-time LiDAR scene completion.