260 citations · 321 across the 45 of their papers we have counts for
63 papers
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…
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…
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…
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…
Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyond
Xin Qiao, Matteo Poggi, Xing Wei +3
Under-display ToF imaging aims to achieve accurate depth sensing through a ToF camera placed beneath a screen panel. However, transparent OLED (TOLED) layers introduce severe degra…
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 3D Gaussian Splatting SLAM for Outdoor Urban Scenes
Xiaohan Li, Ziren Gong, Fabio Tosi +4
3D Gaussian Splatting (3DGS) has recently gained popularity in SLAM applications due to its fast rendering and high-fidelity representation. However, existing 3DGS-SLAM systems hav…
Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos
Ziren Gong, Xiaohan Li, Fabio Tosi +4
We present Ov3R, a novel framework for open-vocabulary semantic 3D reconstruction from RGB video streams, designed to advance Spatial AI. The system features two key components: CL…
DINO-SLAM: DINO-informed RGB-D SLAM for Neural Implicit and Explicit Representations
Ziren Gong, Xiaohan Li, Fabio Tosi +4
This paper presents DINO-SLAM, a DINO-informed design strategy to enhance implicit (Neural Radiance Field -- NeRF) and explicit representations (Gaussian Splatting -- GS) in SLAM s…
NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez, Fabio Tosi, Luigi Di Stefano +36
This paper reports on the NTIRE 2025 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhanc…
ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration
Andrea Conti, Matteo Poggi, Valerio Cambareri +2
Time-of-Flight (ToF) sensors provide efficient active depth sensing at relatively low power budgets; among such designs, only very sparse measurements from low-resolution sensors a…
HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM
Ziren Gong, Fabio Tosi, Youmin Zhang +2
NeRF-based SLAM has recently achieved promising results in tracking and reconstruction. However, existing methods face challenges in providing sufficient scene representation, capt…
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…
Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor
Andrea Conti, Matteo Poggi, Valerio Cambareri +1
High frame rate and accurate depth estimation plays an important role in several tasks crucial to robotics and automotive perception. To date, this can be achieved through ToF and…
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…
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…
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods
Xin Qiao, Matteo Poggi, Pengchao Deng +3
Integrating an RGB camera into a ToF imaging system has become a significant technique for perceiving the real world. The RGB guided ToF imaging system is crucial to several applic…
How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
Fabio Tosi, Youmin Zhang, Ziren Gong +4
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling a…
Range-Agnostic Multi-View Depth Estimation With Keyframe Selection
Andrea Conti, Matteo Poggi, Valerio Cambareri +1
Methods for 3D reconstruction from posed frames require prior knowledge about the scene metric range, usually to recover matching cues along the epipolar lines and narrow the searc…
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…
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes
Chaoqiang Zhao, Matteo Poggi, Fabio Tosi +4
This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture. We ease the learning proce…
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…