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Poster

EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision

Jiawei Yang · Boris Ivanovic · Or Litany · Xinshuo Weng · Seung Wook Kim · Boyi Li · Tong Che · Danfei Xu · Sanja Fidler · Marco Pavone · Yue Wang

Halle B
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Fri 10 May 7:30 a.m. PDT — 9:30 a.m. PDT

Abstract:

We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. Grounded in neural fields, EmerNeRF simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. EmerNeRF hinges upon two core components: First, it stratifies scenes into static and dynamic fields. This decomposition emerges purely from self-supervision, enabling our model to learn from general, in-the-wild data sources. Second, EmerNeRF parameterizes an induced flow field from the dynamic field and uses this flow field to further aggregate multi-frame features, amplifying the rendering precision of dynamic objects. Coupling these three fields (static, dynamic, and flow) enables EmerNeRF to represent highly-dynamic scenes self-sufficiently, without relying on ground truth object annotations or pre-trained models for dynamic object segmentation or optical flow estimation. Our method achieves state-of-the-art performance in sensor simulation, significantly outperforming previous methods when reconstructing static (+2.39 PSNR) and dynamic (+3.25 PSNR) scenes. In addition, to bolster EmerNeRF's semantic generalization, we lift 2D visual foundation model features into 4D space-time and address a general positional bias in modern Transformers, significantly boosting 3D perception performance (e.g., 78.5% relative improvement in occupancy prediction accuracy). Finally, we construct a diverse and challenging 120-sequence dataset to benchmark neural fields under extreme and highly-dynamic settings. Visualizations, code, and data will be anonymously available at https://anonymous.4open.science/r/EmerNeRF_review-003B/

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