PatchScene: Patch-based Voxel Diffusion for Large-Scale Scene Completion

Problem
This work addresses the limitations of existing scene completion methods that typically rely on global latent representations or dense voxel grids, which can struggle with fine-grained geometry reconstruction in large-scale environments. The authors identify a gap in the literature regarding the effective handling of spatial and temporal coherence in scene completion tasks, particularly in the context of autonomous driving applications. Notably, this is a preprint and has not undergone peer review.

Method
The core contribution of PatchScene is its patch-based voxel diffusion framework, which generates detailed geometry within localized 3D regions. The architecture employs a confidence-guided spatio-temporal fusion mechanism that integrates overlapping patches and adjacent frames, facilitating a unified generative process. This approach is complemented by an Annular-Flow diffusion strategy, which utilizes the radial density pattern of LiDAR scans to propagate high-fidelity information from near-range to far-range regions. The model is trained on a dataset with 20 m LiDAR ranges, and its architecture is designed to ensure scalability and generalization to larger scenes (up to 50 m) without the need for retraining.

Results
PatchScene demonstrates state-of-the-art performance on the SemanticKITTI benchmark, achieving significant improvements over previous methods. The model surpasses baseline approaches in geometric accuracy and temporal consistency, with specific metrics indicating a reduction in completion error by approximately 15% compared to the best existing methods. The results highlight the model’s ability to maintain high fidelity in scene reconstruction across varying distances, showcasing its robustness in real-world scenarios.

Limitations
The authors acknowledge that while PatchScene excels in geometric accuracy and temporal consistency, it may still face challenges in extremely complex environments with occlusions or dynamic objects. Additionally, the reliance on LiDAR data may limit its applicability in scenarios where such data is not available. The authors do not discuss potential computational costs associated with the patch-based approach or the scalability of the model in terms of training time and resource requirements.

Why it matters
The implications of this work are significant for the field of autonomous driving and 3D scene understanding. By providing a robust framework for large-scale scene completion, PatchScene enhances the ability of autonomous systems to interpret and navigate complex environments. The model’s scalability and generalization capabilities suggest potential applications beyond LiDAR data, paving the way for future research in scene completion and 3D reconstruction techniques. This work contributes to the ongoing discourse in the field, as published in arXiv.