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20162026
most citedMonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer

260 citations · 321 across the 45 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CV2026

Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction

Ziren Gong, Guo Chen, Yongjia Li +13

4D scene reconstruction aims to recover the evolving geometry, appearance, and motion of dynamic environments from visual observations. Despite substantial progress in neural scene…

cs.CV2026

ProDyGS: Dynamic Gaussian Splatting from a Single Static Monocular Camera

Ugo Leone Cavalcanti, Fabio Tosi, Matteo Poggi +4

We present ProDyGS, a novel dynamic 3D Gaussian Splatting framework for high-quality novel view synthesis from videos captured by a single static camera. While existing methods rel…

cs.CV2026

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

Ziren Gong, Xiaohan Li, Fabio Tosi +4

This paper presents MAGiSt3R, a multi-agent 3D reconstruction framework performing reconstruction and camera tracking for monocular RGB videos at almost 10 FPS. MAGiSt3R relies on…

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

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

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…