We introduce SANPO, a large-scale egocentric video dataset focused on dense
prediction in outdoor environments. It contains stereo video sessions collected
across diverse outdoor environments, as well as rendered synthetic video
sessions. (Synthetic data was provided by Parallel Domain.) All sessions have
(dense) depth and odometry labels. All synthetic sessions and a subset of real
sessions have temporally consistent dense panoptic segmentation labels. To our
knowledge, this is the first human egocentric video dataset with both large
scale dense panoptic segmentation and depth annotations. In addition to the
dataset we also provide zero-shot baselines and SANPO benchmarks for future
research. We hope that the challenging nature of SANPO will help advance the
state-of-the-art in video segmentation, depth estimation, multi-task visual
modeling, and synthetic-to-real domain adaptation, while enabling human
navigation systems.
SANPO is available here:
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SANPO 是一个大规模的自我中心视频数据集，重点关注户外环境中的密集预测。它包含了在各种户外环境中收集的立体视频会话以及渲染的合成视频会话。此外，该数据集还提供了零样本基准和 SANPO 基准，以用于未来的研究。