260 citations · 327 across the 61 of their papers we have counts for
12 papers · 1 filter
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…
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
Youmin Zhang, Fabio Tosi, Stefano Mattoccia +1
Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in ca…
Depth self-supervision for single image novel view synthesis
Giovanni Minelli, Matteo Poggi, Samuele Salti
In this paper, we tackle the problem of generating a novel image from an arbitrary viewpoint given a single frame as input. While existing methods operating in this setup aim at pr…
To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
Marc Botet Colomer, Pier Luigi Dovesi, Theodoros Panagiotakopoulos +6
The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events. However, the high…
Learning Depth Estimation for Transparent and Mirror Surfaces
Alex Costanzino, Pierluigi Zama Ramirez, Matteo Poggi +3
Inferring the depth of transparent or mirror (ToM) surfaces represents a hard challenge for either sensors, algorithms, or deep networks. We propose a simple pipeline for learning…
The Second Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Michaela Trescakova +40
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, includin…
CompletionFormer: Depth Completion with Convolutions and Vision Transformers
Zhang Youmin, Guo Xianda, Poggi Matteo +3
Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth predict…
NeRF-Supervised Deep Stereo
Fabio Tosi, Alessio Tonioni, Daniele De Gregorio +1
We introduce a novel framework for training deep stereo networks effortlessly and without any ground-truth. By leveraging state-of-the-art neural rendering solutions, we generate s…
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
Xin Qiao, Chenyang Ge, Youmin Zhang +4
We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The f…
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez, Alex Costanzino, Fabio Tosi +4
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open…