PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments (2310.00874v1)

Abstract: Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost. Although the recently developed neural radiance fields (NeRF) have shown compelling results in implicit representations, the large-scale 3D scene reconstruction using partially lost LiDAR point cloud data still needs to be explored. To bridge this gap, we propose a novel 3D scene reconstruction framework called parent-child neural radiance field (PC-NeRF). The framework comprises two modules, the parent NeRF and the child NeRF, to simultaneously optimize scene-level, segment-level, and point-level scene representations. Sensor data can be utilized more efficiently by leveraging the segment-level representation capabilities of child NeRFs, and an approximate volumetric representation of the scene can be quickly obtained even with limited observations. With extensive experiments, our proposed PC-NeRF is proven to achieve high-precision 3D reconstruction in large-scale scenes. Moreover, PC-NeRF can effectively tackle situations where partial sensor data is lost and has high deployment efficiency with limited training time. Our approach implementation and the pre-trained models will be available at https://github.com/biter0088/pc-nerf.

• The paper introduces a dual NeRF framework, combining parent and child models to efficiently reconstruct large-scale 3D scenes despite partial LiDAR data loss.
• It employs three innovative depth losses and a two-step depth inference mechanism to ensure both overall scene integrity and detailed segment accuracy.
• Experimental results on KITTI and MaiCity datasets demonstrate superior reconstruction accuracy and robustness compared to traditional NeRF approaches.

An Overview of PC-NeRF: Parent-Child Neural Radiance Fields in Autonomous Driving

The development of efficient 3D scene reconstruction methods is critical in enhancing the capabilities of autonomous vehicles, particularly when sensor data is incomplete. In the paper "PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments," the authors address the challenges posed by partial LiDAR data loss in autonomous driving contexts through the novel Parent-Child Neural Radiance Field (PC-NeRF) framework.

Core Contributions and Methodology

The PC-NeRF framework is structured to simultaneously optimize scene-level, segment-level, and point-level representations of large-scale environments. Divided into two main components—parent NeRF and child NeRF—the approach efficiently captures the holistic structure and finer details within a scene. The parent NeRF encompasses large environmental blocks along an autonomous vehicle's trajectory, whereas the child NeRF focuses on specific geometric segments. This dual representation is pivotal in addressing data loss by ensuring that both local details and overall spatial awareness are maintained.

A significant technical achievement within PC-NeRF is the development of three novel losses: parent NeRF depth loss, child NeRF depth loss, and child NeRF free loss. These facilitate the effective training of the model even when there is partial sensor data loss. Importantly, the paper introduces a two-step depth inference mechanism that underscores the model's efficacy in segmenting and accurately reconstructing intricate 3D scenes from incomplete data inputs.

Experimental Validation and Results

Empirical evaluations were conducted using the MaiCity and KITTI datasets, which offer synthetic and real-world environments, respectively. Notably, PC-NeRF demonstrated high deployment efficiency, achieving significant reconstruction performance after training for just a single epoch across most scenarios. When compared to alternatives like MapRayCasting and the traditional NeRF model, PC-NeRF delivered superior novel LiDAR view synthesis and 3D reconstruction accuracy even under substantial data loss conditions (up to 67%).

The results indicate that PC-NeRF maintains fidelity in 3D environment representation, confirmed by metrics such as Mean Average Error, Accuracy, Chamfer Distance, and F-score. These findings highlight the robustness of PC-NeRF against variations in the density and spread of LiDAR data, which mimics real-world conditions of sensor failures or unfavorable weather conditions.

Implications and Future Directions

By addressing the limitations of current NeRF approaches in autonomous driving, the PC-NeRF framework holds significant implications for real-world deployment. The strong numerical performance in environments prone to data loss positions PC-NeRF as a highly practical solution for real-time autonomous navigation and environmental understanding.

The authors suggest future exploration into the integration of PC-NeRF with object detection and localization systems, aiming to further enhance autonomous vehicles' situational awareness and safety. Moreover, considering the adoption of PC-NeRF in various autonomous platforms, researchers could intensify focus on optimizing the computational demands to further elevate real-time processing capabilities.

Conclusion

The PC-NeRF framework represents a substantial advancement in the domain of 3D scene reconstruction and autonomous vehicle navigation under data constraints. Through innovative structuring and efficient representation strategies, the framework not only addresses current limitations of NeRF approaches but provides a strong foundation for future research and practical applications in dynamically evolving environments. The insights gained from this paper pave the way for integrating even more complex scene processing tasks, pushing the boundaries of autonomous driving technologies.