DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fields
arxiv(2023)
摘要
Advances in neural fields are enabling high-fidelity capture of the shape and
appearance of dynamic 3D scenes. However, their capabilities lag behind those
offered by conventional representations such as 2D videos because of
algorithmic challenges and the lack of large-scale multi-view real-world
datasets. We address the dataset limitation with DiVa-360, a real-world 360
dynamic visual dataset that contains synchronized high-resolution and
long-duration multi-view video sequences of table-scale scenes captured using a
customized low-cost system with 53 cameras. It contains 21 object-centric
sequences categorized by different motion types, 25 intricate hand-object
interaction sequences, and 8 long-duration sequences for a total of 17.4 M
image frames. In addition, we provide foreground-background segmentation masks,
synchronized audio, and text descriptions. We benchmark the state-of-the-art
dynamic neural field methods on DiVa-360 and provide insights about existing
methods and future challenges on long-duration neural field capture.
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