LiNeXt: Revisiting LiDAR Completion with Efficient Non-Diffusion Architectures

TL;DR

LiNeXt introduces a fast, lightweight, non-diffusion architecture for LiDAR scene completion that significantly improves inference speed and accuracy over diffusion-based methods, enabling real-time autonomous vehicle perception.

Contribution

The paper proposes LiNeXt, a novel non-diffusion network with noise denoising and refinement modules, optimized for rapid and accurate LiDAR point cloud completion, addressing computational inefficiencies of prior diffusion models.

Findings

199.8x faster inference on SemanticKITTI

50.7% reduction in Chamfer Distance

uses only 6.1% of parameters compared to LiDiff

Abstract

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.