Autonomous driving paper index
Sky-NeRF: Learning 4D Cloud Topography in a Dynamic Neural Radiance Field
One-line summary
We present Sky-NeRF, a novel method for cloud topography estimation based on Dynamic Neural Radiance Fields.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract. We present Sky-NeRF, a novel method for cloud topography estimation based on Dynamic Neural Radiance Fields. Similar to NeRF, we propose to model the 3D structure of clouds as a radiance field, encoded in the parameters of a neural representation. Our goal is to reconstruct the 3D geometry, appearance, and motion of the cloud using a stereo-video of high-resolution top of the atmosphere radiance images. In this paper, we evaluate a novel way of modeling the dynamic behavior of clouds, with the goal of extracting added-value physical information regarding the cloud such as advection speed and direction, velocity field and cloud trajectories. We investigate how to include a simple physical prior, advection, into the learning system and evaluate its impact. Our results show that Sky-NeRF is able to provide a more complete 4D reconstruction than traditional stereo-matching-based algorithms. Moreover, thanks to a physics-based interpolation, Sky-NeRF is able to generate coherent new images from unseen viewing angles, and at any time between the observed frames.
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