Multi-Level Neural Scene Graphs for Dynamic Urban Environments

• URL: http://arxiv.org/abs/2404.00168v1
• Date: Fri, 29 Mar 2024 21:52:01 GMT
• Title: Multi-Level Neural Scene Graphs for Dynamic Urban Environments
• Authors: Tobias Fischer, Lorenzo Porzi, Samuel Rota Bulò, Marc Pollefeys, Peter Kontschieder,
• Abstract summary: 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.
• Score: 64.26401304233843
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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