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cs.CV2025
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Jannik Endres, Oliver Hahn, Charles Corbière +3
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-…
cs.CV2025
Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios
Charles Corbière, Simon Roburin, Syrielle Montariol +2
While chain-of-thought (CoT) prompting improves reasoning in large language models, its effectiveness in vision-language models (VLMs) remains limited due to over-reliance on textu…
cs.CV2025
Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
Mehdi Zayene, Jannik Endres, Albias Havolli +4
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…