Towards Real-Time and Adaptable LiDAR Scene Completion

Hugging Face Daily Papers Papers
lidar scene-completion real-time adaptive voxel bev autonomous-driving

Summary

RapidLiDAR introduces a real-time LiDAR scene completion method using adaptive initialization and multi-scale reconstruction, achieving state-of-the-art performance 2.3 times faster to match automotive LiDAR rates.

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.
Original Article
View Cached Full Text

Cached at: 08/20/26, 08:05 PM

Paper page - Towards Real-Time and Adaptable LiDAR Scene Completion

Source: https://huggingface.co/papers/2608.16490

Abstract

RapidLiDAR learns adaptive spatial displacements to initialize LiDAR scenes and refines them via multi-scale voxel and BEV features for real-time completion.

LiDAR scene completionis 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, aLiDAR scene completionmethod that treats the initialization itself as a learned, data-driven component. We propose anadaptive initializationmodule that predicts aspatially varying displacementfor 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 amulti-scale reconstructionmodule that further refines point positions by querying multi-scale 3D voxel and2D BEV feature mapsconstructed from the input scan. By replacing point-neighborhood operators such asfarthest point samplingandk-nearest neighbor searchwith 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-timeLiDAR scene completion.

View arXiv pageView PDFGitHubAdd to collection

Get this paper in your agent:

hf papers read 2608\.16490

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2608.16490 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2608.16490 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2608.16490 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

We’re proud to open-source LIDARLearn [R] [D] [P]

Reddit r/MachineLearning

LIDARLearn is an open-source PyTorch library for 3D point cloud deep learning that unifies 56 pre-configured models with built-in cross-validation and automatic publication-ready LaTeX report generation. The framework supports supervised, self-supervised, and parameter-efficient fine-tuning methods across datasets like ModelNet40, ShapeNet, and remote sensing benchmarks.

Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving

arXiv cs.CL

Fast-dDrive is a block-diffusion VLA model for end-to-end autonomous driving that achieves state-of-the-art trajectory accuracy while delivering over 12x throughput speedup over autoregressive baselines, addressing the trade-off between high-fidelity planning and efficient inference for edge deployment.