# RapidLiDAR：迈向实时且自适应的 LiDAR 场景补全

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-17 08:00
- AIHOT 分数：46
- AIHOT 链接：https://aihot.virxact.com/items/cmt1yapqq0bwfroovszg77htt
- 原文链接：https://arxiv.org/abs/2608.16490

## AI 摘要

RapidLiDAR 提出一种 LiDAR 场景补全方法，将初始化本身作为可学习的数据驱动组件，通过自适应初始化模块预测每个输入点的空间变化位移，无需手动噪声调参。在 SemanticKITTI 和 KITTI-360 上，该方法补全性能与最先进技术持平，完整场景补全仅需 0.1 秒，比此前最快方法快 2.3 倍，匹配典型车载 LiDAR 传感器的 10 Hz 采集率。

## 正文

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.