11 papers
Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation
Igor Pavlovic, Thiemo Wandel, Anton Obukhov +6
Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computational photography, and robotics,…
ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device
Fabio Tosi, Luca Bartolomei, Matteo Poggi +1
Monocular depth estimation has seen remarkable progress through foundation models achieving robust zero-shot generalization, yet their computational demands place them far beyond t…
Bidirectional Cross-Modal Prompting for Event-Frame Asymmetric Stereo
Ninghui Xu, Fabio Tosi, Lihui Wang +5
Conventional frame-based cameras capture rich contextual information but suffer from limited temporal resolution and motion blur in dynamic scenes. Event cameras offer an alternati…
EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors
Luca Bartolomei, Fabio Tosi, Matteo Poggi +2
We propose EventHub, a novel framework for training deep-event stereo networks without ground truth annotations from costly active sensors, relying instead on standard color images…
Depth AnyEvent: A Cross-Modal Distillation Paradigm for Event-Based Monocular Depth Estimation
Luca Bartolomei, Enrico Mannocci, Fabio Tosi +2
Event cameras capture sparse, high-temporal-resolution visual information, making them particularly suitable for challenging environments with high-speed motion and strongly varyin…
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
Luca Bartolomei, Fabio Tosi, Matteo Poggi +1
We introduce Stereo Anywhere, a novel stereo-matching framework that combines geometric constraints with robust priors from monocular depth Vision Foundation Models (VFMs). By eleg…
LiDAR-Event Stereo Fusion with Hallucinations
Luca Bartolomei, Matteo Poggi, Andrea Conti +1
Event stereo matching is an emerging technique to estimate depth from neuromorphic cameras; however, events are unlikely to trigger in the absence of motion or the presence of larg…
A Survey on Deep Stereo Matching in the Twenties
Fabio Tosi, Luca Bartolomei, Matteo Poggi
Stereo matching is close to hitting a half-century of history, yet witnessed a rapid evolution in the last decade thanks to deep learning. While previous surveys in the late 2010s…
Active Stereo in the Wild through Virtual Pattern Projection
Luca Bartolomei, Matteo Poggi, Fabio Tosi +2
This paper presents a novel general-purpose guided stereo paradigm that mimics the active stereo principle by replacing the unreliable physical pattern projector with a depth senso…
Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization
Luca Bartolomei, Matteo Poggi, Andrea Conti +2
This paper proposes a new framework for depth completion robust against domain-shifting issues. It exploits the generalization capability of modern stereo networks to face depth co…
Active Stereo Without Pattern Projector
Luca Bartolomei, Matteo Poggi, Fabio Tosi +2
This paper proposes a novel framework integrating the principles of active stereo in standard passive camera systems without a physical pattern projector. We virtually project a pa…