5x Faster LiDAR Scene Completion Brings Real-Time Navigation Closer

By Soham NandiReviewed by Joel ScanlonDec 16 2024

Cutting-edge research introduces ScoreLiDAR, a breakthrough model that completes 3D scenes 5x faster, paving the way for real-time autonomous vehicle perception.

​​​​​​​A demonstration of the LiDAR scene completion examples. Given a sparse LiDAR scan in (a), the model aims to recover the ground-truth dense scene as in (b). In these examples, scans are from SemanticKITTI [1] and KITTI360 [17] dataset. In both cases, LiDiff [24], a SOTA LiDAR scene completion method, requires about 30 seconds as in (c). In comparison, our proposed ScoreLiDAR takes only about 5 seconds in (d), achieving over 5x speedup with improved completion quality indicated by lower Chamfer Distance (CD).

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

In an article recently submitted to the arXiv preprint* server, researchers focused on improving three-dimensional (3D) light detection and ranging (LiDAR) scene completion for autonomous vehicles by addressing the slow sampling speed of diffusion-based methods. The proposed method, ScoreLiDAR, used a novel distillation approach and a novel structural loss — which includes scene-wise loss for global structure and point-wise loss for local geometric details — to enhance efficiency and geometric accuracy. It accelerated scene completion by over five times (from 30.55 to 5.37 seconds per frame) while outperforming state-of-the-art models in quality, as validated on datasets like the semantic Karlsruhe Institute of Technology and Toyota Technological Institute dataset (SemanticKITTI) and KITTI360.

Background

Efficient and accurate environmental recognition using onboard sensors is essential for the safe operation of autonomous vehicles. Among various sensors, 3D LiDAR has become a cornerstone due to its high precision and extended detection range. However, LiDAR-generated 3D point clouds are often sparse, particularly in occluded or complex driving scenarios, leading to challenges in scene comprehension. This sparsity necessitates LiDAR scene completion, which involves reconstructing dense 3D scenes from sparse inputs to enable better perception.

Traditional methods for LiDAR scene completion include depth completion-based and signed distance field (SDF)-based approaches. While effective, these methods often suffer from limitations such as loss of fine details or constraints tied to voxel resolution. Recently, diffusion models have been employed for LiDAR scene completion, demonstrating strong training stability and high-generation quality. Techniques like LiDiff and diffusion semantic scene completion (DiffSSC) have enhanced the richness of generated scenes by refining noise schedules and integrating semantic tasks. However, their slow sampling processes hinder real-time application, which is critical for autonomous vehicles.

To address these gaps, this paper introduced ScoreLiDAR, a novel distillation framework designed for diffusion-based LiDAR scene completion. By incorporating a distillation framework to train a streamlined student model from a pre-trained teacher diffusion model and structural loss for geometric precision, ScoreLiDAR significantly accelerated completion times while maintaining superior scene quality, as demonstrated through extensive experiments.

Foundations and Methodology

The researchers delved into 3D LiDAR scene completion using diffusion models, which simulate complex point cloud data by progressively adding and removing noise. Diffusion models consist of two phases: the forward diffusion process, which adds noise to the data over timesteps, and the reverse process, which denoises to generate a complete scene. The method uses predefined noise scales and optimized models to predict added noise, enabling it to produce high-quality reconstructions efficiently.

The sparse nature of scans poses challenges for LiDAR data. Standard diffusion approaches tend to lose critical details when directly normalized. To counter this, LiDiff introduces local noise offsets that preserve spatial fidelity by incrementally perturbing individual points. Starting with an augmented scan, noisy point clouds are progressively refined through denoising, producing complete and realistic scenes.

The authors proposed ScoreLiDAR, which optimized scene completion by distilling pre-trained diffusion models into a streamlined student model with fewer sampling steps. This novel framework minimizes divergence between the teacher and student model distributions while ensuring faster inference. A structural loss function, comprising scene-wise and point-wise components, enhanced the realism of completed scenes. The scene-wise loss constrained global structures, while the point-wise loss focused on preserving local geometric details.

The approach ensured efficient, high-quality LiDAR scene reconstructions, combining global accuracy with intricate local details while significantly reducing computational overhead.

Experiments and Analysis

The experiment evaluated the performance of ScoreLiDAR, comparing it to state-of-the-art models like lightweight multiscale 3D semantic completion network (LMSCNet), locally conditioned eikonal formulation (LODE), MID, Point-Voxel diffusion (PVD), and LiDiff. First, ScoreLiDAR’s scene completion capabilities were tested on SemanticKITTI and KITTI-360 datasets, where it surpassed other models, including the teacher model LiDiff. With a structural loss integrating scene-wise and point-wise terms, ScoreLiDAR captured geometric structures more effectively, achieving an 8% improvement in Chamfer distance (CD) and a 4% improvement in Jensen-Shannon divergence (JSD) compared to LiDiff while completing scenes approximately five times faster. On KITTI-360, ScoreLiDAR further demonstrated a 12% improvement in CD and 2% in JSD over LiDiff.

Ablation studies confirmed the significance of structural loss, as its absence resulted in lower performance across metrics. Additionally, experiments with varying sampling steps revealed that ScoreLiDAR maintained superior performance compared to LiDiff, even at reduced steps. Single-step sampling completed scenes in just 1.1 seconds, effectively balancing quality and speed.

Qualitative analyses highlighted ScoreLiDAR’s ability to generate detailed scene completions closer to the ground truth, with better object clarity and minimal discrepancies. A user study further validated these findings, with 65% of participants preferring ScoreLiDAR's outputs over LiDiff, underscoring its superior perceptual quality.

Conclusion

In conclusion, ScoreLiDAR significantly enhanced 3D LiDAR scene completion for autonomous vehicles by addressing the slow sampling speed of diffusion-based methods. Through a novel distillation approach and the incorporation of structural loss, it accelerated scene completion while maintaining high geometric accuracy. ScoreLiDAR outperformed existing models, including LiDiff, with faster completion times and superior scene quality, as demonstrated through extensive experiments on datasets like SemanticKITTI and KITTI-360. The method’s ability to explore single-step sampling, achieving completion in just 1.1 seconds, makes it a promising solution for real-time LiDAR scene reconstruction — a critical requirement for autonomous vehicle perception systems.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.