3D LiDAR scene completion from point clouds is a fundamental component of perception systems in autonomous vehicles. Previous methods have predominantly employed diffusion models for high-fidelity reconstruction. However, their multi-step iterative sampling incurs significant computational overhead, limiting its real-time applicability. To address this, we propose LiNeXt-a lightweight, non-diffusion network optimized for rapid and accurate point cloud completion. Specifically, LiNeXt first applies the Noise-to-Coarse (N2C) Module to denoise the input noisy point cloud in a single pass, thereby obviating the multi-step iterative sampling of diffusion-based methods. The Refine Module then takes the coarse point cloud and its intermediate features from the N2C Module to perform more precise refinement, further enhancing structural completeness. Furthermore, we observe that LiDAR point clouds exhibit a distance-dependent spatial distribution, being densely sampled at proximal ranges and sparsely sampled at distal ranges. Accordingly, we propose the Distance-aware Selected Repeat strategy to generate a more uniformly distributed noisy point cloud. On the SemanticKITTI dataset, LiNeXt achieves a 199.8x speedup in inference, reduces Chamfer Distance by 50.7%, and uses only 6.1% of the parameters compared with LiDiff. These results demonstrate the superior efficiency and effectiveness of LiNeXt for real-time scene completion.

翻译：基于点云的3D激光雷达场景补全是自动驾驶感知系统的核心组成部分。现有方法主要采用扩散模型实现高保真重建，但其多步迭代采样过程计算开销显著，限制了实时应用。为此，我们提出LiNeXt——一种专为快速精准点云补全优化的轻量化非扩散网络。具体而言，LiNeXt首先通过噪声到粗粒度模块对输入噪声点云进行单次去噪，从而避免基于扩散方法的多步迭代采样。随后，精细化模块利用粗粒度点云及其从噪声到粗粒度模块提取的中间特征进行更精确的细化，进一步提升结构完整性。此外，我们观察到激光雷达点云具有距离相关的空间分布特性：近程区域采样密集，远程区域采样稀疏。基于此，我们提出距离感知选择性重复策略，以生成空间分布更均匀的噪声点云。在SemanticKITTI数据集上，LiNeXt相比LiDiff实现了199.8倍的推理加速，倒角距离降低50.7%，参数量仅需其6.1%。这些结果证明了LiNeXt在实时场景补全任务中卓越的效率和性能。