Multi-Level Neural Scene Graphs for Dynamic Urban Environments (2404.00168v1)

Abstract: We estimate the radiance field of large-scale dynamic areas from multiple vehicle captures under varying environmental conditions. Previous works in this domain are either restricted to static environments, do not scale to more than a single short video, or struggle to separately represent dynamic object instances. To this end, we present a novel, decomposable radiance field approach for dynamic urban environments. We propose a multi-level neural scene graph representation that scales to thousands of images from dozens of sequences with hundreds of fast-moving objects. To enable efficient training and rendering of our representation, we develop a fast composite ray sampling and rendering scheme. To test our approach in urban driving scenarios, we introduce a new, novel view synthesis benchmark. We show that our approach outperforms prior art by a significant margin on both established and our proposed benchmark while being faster in training and rendering.

• The paper introduces a decomposable radiance field approach using multi-level neural scene graphs to effectively represent dynamic urban scenes.
• It implements a fast composite ray sampling strategy that significantly accelerates training and rendering compared to conventional methods.
• Evaluations on a novel urban benchmark demonstrate enhanced view synthesis performance with improved PSNR, SSIM, and LPIPS metrics.

Multi-Level Neural Scene Graphs for Dynamic Urban Environments

Introduction

Dynamic urban environments pose a complex challenge for radiance field estimation due to their inherent variability and the presence of multiple, fast-moving objects. Traditional methods either focus on static scenes or deal inadequately with dynamic entities, limiting their applicability for realistic novel view synthesis in city-scale scenarios. Addressing these limitations, "Multi-Level Neural Scene Graphs for Dynamic Urban Environments" introduces a novel decomposable radiance field approach designed to scale efficiently to large geographic areas replete with dynamic entities under varying conditions.

Contributions

The paper's primary contributions are threefold:

• The introduction of a multi-level neural scene graph representation that is capable of handling large datasets comprising thousands of images across multiple sequences, with hundreds of fast-moving objects.
• The development of a fast composite ray sampling and rendering scheme, specifically designed to facilitate efficient training and rendering for the proposed representation.
• The creation of a novel view synthesis benchmark tailored for urban driving scenarios, enabling realistic and application-driven evaluations of radiance field reconstruction in dynamic environments.

Methodology

Scene Graph Representation

The foundation of the approach is a multi-level scene graph that organizes the environment into dynamic objects, sequences, and camera nodes, all connected within a hierarchical structure allowing for detailed localization and identification of each entity in the 3D space. The global frame at the root node unifies these elements, facilitating coherent scene representation. This graph is key to distinguishing between static and dynamic components effectively and overcoming the limitations of earlier works.

Efficiency through Ray Sampling and Rendering

Given the large-scale nature of urban environments, rendering efficiency is paramount. The paper addresses this by implementing a composite ray sampling strategy that drastically accelerates both the training and rendering processes, as opposed to traditional methods that suffer from inefficiencies due to either sparse sampling or separate node processing.

Benchmark for Urban Environments

To facilitate effective evaluation, the paper introduces a comprehensive benchmark based on the Argoverse 2 dataset, incorporating various environmental conditions across two urban areas. This benchmark is pivotal in stressing the method's robustness and scalability while providing a clear path for comparative analysis against existing methods.

Evaluation and Results

Through extensive testing, the proposed approach significantly outperformed existing methods on both the established and newly proposed benchmarks, particularly in terms of view synthesis quality under dynamic conditions. Results showed notable improvements in PSNR, SSIM, and LPIPS metrics, evidencing both the method’s precision and its practical efficiency during training and rendering phases.

Implications and Future Directions

The research presents a significant step forward in the radiance field reconstruction of dynamic urban environments, indicating promising applications in autonomous driving, city-scale mapping, and mixed reality scenarios. By intricately representing dynamic objects within a scalable framework and demonstrating efficiency in large datasets, this work paves the way for future developments in the field.

Speculatively, the introduction of a multi-level scene graph might herald a new focus on higher-level decompositions in scene understanding, potentially leading to even more sophisticated methods for dealing with the intricacies of dynamic environments. Furthermore, the benchmark established herein offers a robust foundation for future research, highlighting the importance of real-world applicability in the development of novel view synthesis methods.

Closing Remarks

Overcoming the challenge of reconstructing radiance fields in dynamic urban environments calls for innovative approaches that can effectively handle large-scale, complex data. This paper's contributions, notably the multi-level neural scene graph, represent a significant advancement towards this goal, offering enhanced realism and efficiency. The accompanying benchmark further strengthens the methodology's value, providing a comprehensive tool for ongoing and future research in the field.