collaborators

20 papers

cs.CV2026

MAGiSt3R: Multi-Agent Feed-forward 3D Reconstruction from Monocular RGB Videos

Ziren Gong, Xiaohan Li, Fabio Tosi +4

The paper introduces MAGiSt3R, a multi-agent framework that reconstructs 3D scenes and tracks camera pose from monocular RGB videos in near real-time using feed-forward models and…

cs.CV2026

DINO-SLAM: DINO-informed RGB-D SLAM for Neural Implicit and Explicit Representations

Ziren Gong, Xiaohan Li, Fabio Tosi +4

The paper introduces DINO-SLAM, a system that combines DINO semantic features with a geometry encoder to improve both neural implicit (NeRF) and explicit (Gaussian Splatting) SLAM…

cs.CV2026

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…

cs.CV2026

StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space

Tjark Behrens, Anton Obukhov, Bingxin Ke +3

We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without explicit depth or warpin…

cs.CV2026

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…

cs.CV2026

FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM

Yuchen Wu, Jiahe Li, Fabio Tosi +3

We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robus…