Despite considerable progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation, consisting of 40K frames from video sequences across diverse environments, including crowded indoor and outdoor scenes with diverse lighting conditions. Collected using two 360{\deg} cameras in a top-bottom setup and a LiDAR sensor, the dataset includes accurate depth and disparity labels by projecting 3D point clouds onto equirectangular images. Additionally, we provide an augmented training set with a significantly increased label density by using depth completion. We benchmark leading stereo depth estimation models for both standard and omnidirectional images. The results show that while recent stereo methods perform decently, a significant challenge persists in accurately estimating depth in omnidirectional imaging. To address this, we introduce necessary adaptations to stereo models, achieving improved performance.

本研究解决了全向成像领域在立体深度估计中数据不足的问题，提出了Helvipad数据集，该数据集包含来自各种环境的40K帧视频序列。通过准确的深度和视差标签，研究显示现有立体方法在全向成像中表现尚可，但仍然存在准确估计深度的重大挑战，因此提出了对立体模型的必要改进，从而提升了性能。

Helvipad：用于全向立体深度估计的真实世界数据集