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
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 (+2.93 PSNR) and dynamic (+3.70 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., 37.50% relative improvement in occupancy prediction accuracy on
average). Finally, we construct a diverse and challenging 120-sequence dataset
to benchmark neural fields under extreme and highly-dynamic settings.