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Filippo Aleotti

13 accepted papers

2026

Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images

Matias Turkulainen, Akshay Krishnan, Filippo Aleotti, Mohamed Sayed, Guillermo Garcia-Hernando, Juho Kannala +3
CVPR 2026

We present Cross-View Splatter, a feed-forward method that predicts pixel-aligned Gaussian splats for outdoor scenes captured at ground level and by satellite. Faithful reconstructions require good camera coverage, but ground imagery is time-consuming and hard to capture at scale for large outdoor s

Cited by 0Source ↗code ↗Scholar ↗
2025

PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes

Ahmed Abdelreheem, Filippo Aleotti, Jamie Watson, Zawar Qureshi, Abdelrahman Eldesokey, Peter Wonka +3
ICCV 2025poster

We introduce the task of Language-Guided Object Placement in Real 3D Scenes. Given a 3D reconstructed point-cloud scene, a 3D asset, and a natural-language instruction, the goal is to place the asset so that the instruction is satisfied. The task demands tackling four intertwined challenges: (a) one…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings

Jamie Watson, Filippo Aleotti, Mohamed Sayed, Zawar Qureshi, Oisin Mac Aodha, Gabriel Brostow +2
CVPR 2024poster

Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular cluster…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

DoubleTake: Geometry Guided Depth Estimation

Mohamed Sayed*, Filippo Aleotti, Jamie Watson, Zawar Qureshi, Guillermo Garcia-Hernando, Gabriel Brostow +2
ECCV 2024poster

"Estimating depth from a sequence of posed RGB images is a fundamental computer vision task, with applications in augmented reality, path planning etc. Prior work typically makes use of previous frames in a multi view stereo framework, relying on matching textures in a local neighborhood. In contras…

Cited by 1Source ↗PDF ↗Scholar ↗
2022

Unsupervised confidence for LiDAR depth maps and applications

Andrea Conti, Matteo Poggi, Filippo Aleotti, Stefano Mattoccia
IROS 2022poster

Depth perception is pivotal in many fields, such as robotics and autonomous driving, to name a few. Consequently, depth sensors such as LiDARs rapidly spread in many applications. The 3D point clouds generated by these sensors must often be coupled with an RGB camera to understand the framed scene s…

Cited by 13Source ↗code ↗Scholar ↗
2020

Distilled Semantics for Comprehensive Scene Understanding from Videos

Fabio Tosi, Filippo Aleotti, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Luigi Di Stefano +1
CVPR 2020poster

Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and motion (optical flow) from a monocular video without any explicit supervision from ground truth annotations, particularly hard to source for th…

Cited by 91PDF ↗code ↗Scholar ↗
2020

On the Uncertainty of Self-Supervised Monocular Depth Estimation

Matteo Poggi, Filippo Aleotti, Fabio Tosi, Stefano Mattoccia
CVPR 2020poster

Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. Despite the astonishing results yielded by such methodologies, learning to reason about the uncertainty of the estimated depth maps is of paramount importance for pr…

Cited by 309PDF ↗code ↗Scholar ↗
2020

Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation

Filippo Aleotti, Fabio Tosi, Li Zhang, Matteo Poggi, Stefano Mattoccia
ECCV 2020poster

In many fields, self-supervised learning solutions are rapidly evolving and filling the gap with supervised approaches. This fact occurs for depth estimation based on either monocular or stereo, with the latter often providing a valid source of self-supervision for the former. In contrast, to soften…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2020

Self-adapting confidence estimation for stereo

Matteo Poggi, Filippo Aleotti, Fabio Tosi, Giulio Zaccaroni, Stefano Mattoccia
ECCV 2020poster

Estimating the confidence of disparity maps inferred by a stereo algorithm has become a very relevant task in the years, due to the increasing number of applications leveraging such cue. Although self-supervised learning has recently spread across many computer vision tasks, it has been barely consi…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2019

Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge

Fabio Tosi, Filippo Aleotti, Matteo Poggi, Stefano Mattoccia
CVPR 2019poster

Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be learned without direct supervision from ground truth labels leveraging image synthesis on sequences or stereo pairs. Focusing on this second…

Cited by 278PDF ↗code ↗Scholar ↗
2018

Towards Real-Time Unsupervised Monocular Depth Estimation on CPU

Matteo Poggi, Filippo Aleotti, Fabio Tosi, Stefano Mattoccia
IROS 2018poster

Unsupervised depth estimation from a single image is a very attractive technique with several implications in robotic, autonomous navigation, augmented reality and so on. This topic represents a very challenging task and the advent of deep learning enabled to tackle this problem with excellent resul…

Cited by 189Source ↗code ↗Scholar ↗