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PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments (2402.09325v1)

Published 14 Feb 2024 in cs.CV and cs.RO

Abstract: Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit representations remain a significant bottleneck towards representing the reconstructed and synthetic scenes at unlimited resolution. Although the recently developed neural radiance fields (NeRF) have shown compelling results in implicit representations, the problem of large-scale 3D scene reconstruction and novel view synthesis using sparse LiDAR frames remains unexplored. To bridge this gap, we propose a 3D scene reconstruction and novel view synthesis framework called parent-child neural radiance field (PC-NeRF). Based on its two modules, parent NeRF and child NeRF, the framework implements hierarchical spatial partitioning and multi-level scene representation, including scene, segment, and point levels. The multi-level scene representation enhances the efficient utilization of sparse LiDAR point cloud data and enables the rapid acquisition of an approximate volumetric scene representation. With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes. Moreover, PC-NeRF can effectively handle situations with sparse LiDAR frames and demonstrate high deployment efficiency with limited training epochs. Our approach implementation and the pre-trained models are available at https://github.com/biter0088/pc-nerf.

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Summary

  • The paper introduces PC-NeRF, which uses hierarchical spatial partitioning and multi-level representation to reconstruct large-scale 3D driving scenes from sparse LiDAR data.
  • The method partitions scenes into parent and child NeRFs, enabling efficient and scalable processing of outdoor environments in autonomous driving.
  • Experimental results on KITTI and MaiCity datasets demonstrate high accuracy and swift deployment potential with minimal training epochs.

Enhancing Large-Scale 3D Scene Reconstruction and Novel View Synthesis with PC-NeRF

Introduction to PC-NeRF

The task of large-scale 3D scene reconstruction and novel view synthesis is vital for autonomous driving systems, demanding high precision and efficiency in the utilisation of sparse LiDAR data. Traditional methods, although capable of visual depiction, are challenged by the discrete nature of explicit representations, limiting the potential for scenes to be rendered at unlimited resolutions. The introduction of neural radiance fields (NeRF) offers a promising direction, leveraging a continuous, differentiable framework for scene representation. However, the specific challenges of applying NeRF techniques to outdoor environments with sparse LiDAR frames remain underexplored.

To address this gap, we present the Parent-Child Neural Radiance Fields (PC-NeRF) framework. PC-NeRF innovates on the standard NeRF approach by implementing hierarchical spatial partitioning and multi-level scene representation tailored to the characteristics of outdoor driving environments and the sparse nature of LiDAR data typically available in autonomous driving scenarios.

Hierarchical Spatial Partitioning

PC-NeRF divides the driving environment into multiple large blocks, termed parent NeRFs, which are further partitioned into geometric segments represented by child NeRFs. This structure allows for efficient spatial organization and a scalable approach to representing extensive outdoor scenes, enabling the specific challenges posed by sparse LiDAR data to be managed more effectively.

Multi-level Scene Representation

Building on the hierarchical spatial organization, PC-NeRF employs a multi-level scene representation strategy. This innovative approach optimizes the scene representation at various levels of granularity—from the broad scope of the scene level down to the precise details at the point level. Such a multi-faceted framework aids in swiftly capturing the intricate details of large-scale environments even when faced with the inherent limitations of sparse LiDAR frames.

Experimental Validation

Extensive experiments confirm that PC-NeRF achieves notable accuracy in novel LiDAR view synthesis and 3D scene reconstruction across various test scenes from the KITTI and MaiCity datasets. Noteworthy is PC-NeRF's capacity to efficiently handle sparse LiDAR frames, embodying a significant step forward in the practical application of NeRF techniques to outdoor autonomous driving scenarios. Additionally, PC-NeRF demonstrates excellent deployment efficiency, able to produce high-quality reconstructions with a minimal number of training epochs, which denotes a critical advancement in the field, enhancing the swift deployment potential of NeRF-based methods in real-world applications.

Implications and Future Directions

PC-NeRF's introduction of hierarchical spatial partitioning and multi-level scene representation presents a robust framework for efficiently tackling the challenges of large-scale 3D scene reconstruction and novel view synthesis with sparse LiDAR data. The framework's capabilities in handling sparse LiDAR frames open potential avenues for further research, particularly concerning the integration of PC-NeRF with real-time object detection and localization processes in autonomous driving systems. The exploration of these opportunities could further solidify the role of PC-NeRF within the broader ecosystem of autonomous driving technologies, pushing the boundaries of what is achievable in real-world applications.

In conclusion, PC-NeRF represents a significant advance in the ongoing development of techniques for 3D scene reconstruction and novel view synthesis, especially in the context of autonomous driving. Its ability to efficiently process sparse LiDAR frames while maintaining high precision in scene reconstruction underscores the framework's potential for wide-ranging applications in autonomous vehicle navigation and beyond.

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