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Jannik Endres

2 accepted papers

2025

Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model

IROS 2025

Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps without relying on expens

Cited by 0SourcecodeScholar
2025

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

CVPR 2025highlight

Despite 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, featuring 40K video frames from video sequences across diverse environments…