Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation

• URL: http://arxiv.org/abs/2411.18335v1
• Date: Wed, 27 Nov 2024 13:34:41 GMT
• Title: Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
• Authors: Mehdi Zayene, Jannik Endres, Albias Havolli, Charles Corbière, Salim Cherkaoui, Alexandre Kontouli, Alexandre Alahi,
• Abstract summary: We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation. The dataset includes accurate depth and disparity labels by projecting 3D point clouds onto equirectangular images. We benchmark leading stereo depth estimation models for both standard and omnidirectional images.
• Score: 83.841877607646
• License:
• Abstract: 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.

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