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Matteo Poggi

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16 papers

2 author rows

AAAI Conference 2026 Conference Paper

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

Yuchen Wu
Jiahe Li
Fabio Tosi
Matteo Poggi
Jin Zheng
Xiao Bai

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 robust tracking and mapping. Our core idea is to bridge flow estimation with geometric reasoning by leveraging the guidance from foundation depth models. To this end, we first develop a Hybrid Flow Network that produces geometry-aware correspondences, enabling consistent depth and pose inference across diverse keyframes. To enforce global consistency, we propose a Bi-Consistent Bundle Adjustment Layer that jointly optimizes keyframe pose and depth under multi-view constraints. Furthermore, we introduce a Reliability-Aware Refinement mechanism that dynamically adapts the flow update process by distinguishing between reliable and uncertain regions, forming a closed feedback loop between matching and optimization. Extensive experiments demonstrate that FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS, demonstrating strong generalization to various scenarios and practical applicability of our method.

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ICRA Conference 2025 Conference Paper

Drive with the Flow

Enrico Mannocci
Matteo Poggi
Stefano Mattoccia

End-to-end autonomous driving systems have recently made rapid progress, thanks to simulators such as CARLA. They can drive without infraction of common driving rules on uncongested roads but are still struggling with dense traffic scenarios. We conjecture that this occurs because it lacks understanding of the dynamics of the surrounding vehicles, caused by the absence of explicit short-term memory within the perception path of end- to-end models. To address this challenge, we revise the perception module to explicitly model temporal information, by extending it with an auxiliary task that is well-known in computer vision research: optical flow. We generate a novel benchmark using the CARLA simulator to train our model, FlowFuser, and prove its superior ability to avoid collisions with other agents on the road.

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NeurIPS Conference 2025 Conference Paper

Eve3D: Elevating Vision Models for Enhanced 3D Surface Reconstruction via Gaussian Splatting

Jiawei Zhang
Youmin Zhang
Fabio Tosi
Meiying Gu
Jiahe Li
Xiaohan Yu
Jin Zheng
Xiao Bai

We present Eve3D, a novel framework for dense 3D reconstruction based on 3D Gaussian Splatting (3DGS). While most existing methods rely on imperfect priors derived from pre-trained vision models, Eve3D fully leverages these priors by jointly optimizing both them and the 3DGS backbone. This joint optimization creates a mutually reinforcing cycle: the priors enhance the quality of 3DGS, which in turn refines the priors, further improving the reconstruction. Additionally, Eve3D introduces a novel optimization step based on bundle adjustment, overcoming the limitations of the highly local supervision in standard 3DGS pipelines. Eve3D achieves state-of-the-art results in surface reconstruction and novel view synthesis on the Tanks & Temples, DTU, and Mip-NeRF360 datasets. while retaining fast convergence, highlighting an unprecedented trade-off between accuracy and speed.

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ICRA Conference 2025 Conference Paper

HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM

Ziren Gong
Fabio Tosi
Youmin Zhang 0008
Stefano Mattoccia
Matteo Poggi

NeRF-based SLAM has recently achieved promising results in tracking and reconstruction. However, existing methods face challenges in providing sufficient scene representation, capturing structural information, and maintaining global consistency in scenes emerging significant movement or being forgotten. To this end, we present HS-SLAM to tackle these problems. To enhance scene representation capacity, we propose a hybrid encoding network that combines the complementary strengths of hash-grid, tri-planes, and one-blob, improving the completeness and smoothness of reconstruction. Additionally, we introduce structural supervision by sampling patches of non-local pixels rather than individual rays to better capture the scene structure. To ensure global consistency, we implement an active global bundle adjustment (BA) to eliminate camera drifts and mitigate accumulative errors. Experimental results demonstrate that HS-SLAM outperforms the baselines in tracking and reconstruction accuracy while maintaining the efficiency required for robotics.

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ICRA Conference 2025 Conference Paper

Lightstereo: Channel Boost is All You Need for Efficient 2D Cost Aggregation

Xianda Guo
Chenming Zhang
Youmin Zhang 0008
Wenzhao Zheng
Dujun Nie
Matteo Poggi
Long Chen 0005

We present LightStereo, a cutting-edge stereomatching network crafted to accelerate the matching process. Departing from conventional methodologies that rely on aggregating computationally intensive 4D costs, LightStereo adopts the 3D cost volume as a lightweight alternative. While similar approaches have been explored previously, our breakthrough lies in enhancing performance through a dedicated focus on the channel dimension of the 3D cost volume, where the distribution of matching costs is encapsulated. Our exhaustive exploration has yielded plenty of strategies to amplify the capacity of the pivotal dimension, ensuring both precision and efficiency. We compare the proposed LightStereo with existing state-of-the-art methods across various benchmarks, which demonstrate its superior performance in speed, accuracy, and resource utilization. LightStereo achieves a competitive EPE metric in the SceneFlow datasets while demanding a minimum of only 22 GFLOPs and 17 ms of runtime, and ranks 1st on KITTI 2015 among real-time models. Our comprehensive analysis reveals the effect of 2 D cost aggregation for stereo matching, paving the way for realworld applications of efficient stereo systems. Code is available at https://github.com/XiandaGuo/OpenStereo.

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IROS Conference 2025 Conference Paper

Self-supervised Monocular Depth Estimation for Dynamic Objects with Ground Propagation

Huan Li
Matteo Poggi
Fabio Tosi
Stefano Mattoccia

Self-supervised single-view depth estimation, trained on video sequences, faces significant challenges when dynamic objects are present in the training data, as they violate the basic multi-view geometry assumptions used to compute photometric losses. We propose a novel approach that leverages the relationship between the depth of moving objects and their ground contact points. By iteratively propagating ground features to moving targets in perceptual layers, we recalibrate the depth of dynamic entities while preserving details. Our method maintains the end-to-end training paradigm without additional networks or complex training procedures. Our experiments demonstrate that our method achieves state-of-the-art performance when estimating depth for dynamic objects and attains superior generalization compared to existing approaches. The relevant experimental code can be accessed at: https://github.com/LiHuanLi/GroundMono

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IROS Conference 2024 Conference Paper

Exploring Few-Beam LiDAR Assistance in Self-Supervised Multi-Frame Depth Estimation

Rizhao Fan
Matteo Poggi
Stefano Mattoccia

Self-supervised multi-frame depth estimation methods only require unlabeled monocular videos for training. However, most existing methods face challenges, including accuracy degradation caused by moving objects in dynamic scenes and scale ambiguity due to the absence of real-world references. In this field, the emergence of low-cost LiDAR sensors highlights the potential to improve the robustness of multi-frame depth estimation by exploiting accurate sparse measurements at the correct scale. Moreover, the LiDAR ranging points often intersect moving objects, providing more precise depth cues for them. This paper explores the impact of few-beam LiDAR data on self-supervised multi-frame depth estimation, proposing a method that fuses multi-frame matching and sparse depth features. It significantly enhances depth estimation robustness, particularly in scenarios involving moving objects and textureless backgrounds. We demonstrate the effectiveness of our approach through comprehensive experiments, showcasing its potential to address the limitations of existing methods and paving the way for more robust and reliable depth estimation based on this paradigm.

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IROS Conference 2024 Conference Paper

MaskingDepth: Masked Consistency Regularization for Semi-Supervised Monocular Depth Estimation

Jongbeom Baek
Gyeongnyeon Kim
Seonghoon Park 0002
Honggyu An
Matteo Poggi
Seungryong Kim

We propose MaskingDepth, a semi-supervised learning framework for monocular depth estimation. MaskingDepth is designed to enforce consistency between the depths obtained from strongly-augmented images and the pseudo-depths derived from weakly-augmented images, which enables mitigating the reliance on large ground-truth depth quantities. In this framework, we leverage uncertainty estimation to only retain high-confident depth predictions from the weakly-augmented branch as pseudo-depths. We also present a novel data augmentation, dubbed K-way disjoint masking, that takes advantage of a naïve token masking strategy as an augmentation, while avoiding its scale ambiguity problem between depths from weakly-and strongly-augmented branches and risk of missing small-scale objects. Experiments on KITTI and NYU-Depth-v2 datasets demonstrate the effectiveness of each component, its robustness to the use of fewer depth-annotated images, and superior performance compared to other state-of-the-art semi-supervised learning methods for monocular depth estimation.

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IROS Conference 2023 Conference Paper

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 predicting the target view depth map to guide the synthesis, without explicit supervision over such a task, we jointly optimize our framework for both novel view synthesis and depth estimation to unleash the synergy between the two at its best. Specifically, a shared depth decoder is trained in a self-supervised manner to predict depth maps that are consistent across the source and target views. Our results demonstrate the effectiveness of our approach in addressing the challenges of both tasks allowing for higher-quality generated images, as well as more accurate depth for the target viewpoint.

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IROS Conference 2023 Conference Paper

TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network

Youmin Zhang 0008
Matteo Poggi
Stefano Mattoccia

We present TemporalStereo, a coarse-to-fine stereo matching network that is highly efficient, and able to effectively exploit the past geometry and context information to boost matching accuracy. Our network leverages sparse cost volume and proves to be effective when a single stereo pair is given. However, its peculiar ability to use spatio-temporal information across stereo sequences allows TemporalStereo to alleviate problems such as occlusions and reflective regions while enjoying high efficiency also in this latter case. Notably, our model - trained once with stereo videos - can run in both single-pair and temporal modes seamlessly. Experiments show that our network relying on camera motion is robust even to dynamic objects when running on videos. We validate TemporalStereo through extensive experiments on synthetic (SceneFlow, TartanAir) and real (KITTI 2012, KITTI 2015) datasets. Our model achieves state-of-the-art performance on any of these datasets.

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ICRA Conference 2022 Conference Paper

Meta-confidence estimation for stereo matching

Seungryong Kim
Matteo Poggi
Sunok Kim
Kwanghoon Sohn
Stefano Mattoccia

We propose a novel framework to estimate the confidence of a disparity map taking into account, for the first time, the uncertainty affecting the confidence estimation process itself. Conversely to other tasks such as disparity estimation, the uncertainty of confidence directly hints that the confidence should be increased if initially low, but with high uncertainty, decreased otherwise. By modelling such a cue in the form of a second-level confidence, or meta-confidence, our solution allows for finding incorrect predictions inferred by confidence estimator and for learning a correction for them. Our strategy is suited for any state-of-the-art method known in literature, either implemented using random forest classifiers or deep neural networks. Especially, for deep neural networks-based models, we present a multi-headed confidence estimator followed by an uncertainty network, so as to predict mean confidence and meta-confidence within a single network without the cost of lower accuracy, a known limitation in literature for uncertainty estimation. Experimental results on a variety of stereo algorithms and confidence estimation models prove that the modeled meta-confidence is meaningful of the reliability of the estimated confidence and allows for refining it.

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IROS Conference 2022 Conference Paper

Multi-View Guided Multi-View Stereo

Matteo Poggi
Andrea Conti 0002
Stefano Mattoccia

This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acquisition. Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network by modulating the plane-sweep cost volume built during the forward step, enabling us to infer constantly much more accurate depth maps. Moreover, since multiple viewpoints can provide additional depth measurements, we propose a multi-view guidance strategy that increases the density of the sparse points used to guide the network, thus leading to even more accurate results. We evaluate our Multi-View Guided framework within a variety of state-of-the-art deep multi-view stereo networks, demonstrating its effectiveness at improving the results achieved by each of them on BlendedMVG and DTU datasets.

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IROS Conference 2022 Conference Paper

Unsupervised confidence for LiDAR depth maps and applications

Andrea Conti 0002
Matteo Poggi
Filippo Aleotti
Stefano Mattoccia

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 semantically. Usually, the former is projected over the camera image plane, leading to a sparse depth map. Unfortunately, this process, coupled with the intrinsic issues affecting all the depth sensors, yields noise and gross outliers in the final output. Purposely, in this paper, we propose an effective unsupervised framework aimed at explicitly addressing this issue by learning to estimate the confidence of the LiDAR sparse depth map and thus allowing for filtering out the outliers. Experimental results on the KITTI dataset highlight that our framework excels for this purpose. Moreover, we demonstrate how this achievement can improve a wide range of tasks.

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AAAI Conference 2020 Conference Paper

Learning End-to-End Scene Flow by Distilling Single Tasks Knowledge

Filippo Aleotti
Matteo Poggi
Fabio Tosi
Stefano Mattoccia

Scene ﬂow is a challenging task aimed at jointly estimating the 3D structure and motion of the sensed environment. Although deep learning solutions achieve outstanding performance in terms of accuracy, these approaches divide the whole problem into standalone tasks (stereo and optical ﬂow) addressing them with independent networks. Such a strategy dramatically increases the complexity of the training procedure and requires power-hungry GPUs to infer scene ﬂow barely at 1 FPS. Conversely, we propose DWARF, a novel and lightweight architecture able to infer full scene ﬂow jointly reasoning about depth and optical ﬂow easily and elegantly trainable end-to-end from scratch. Moreover, since ground truth images for full scene ﬂow are scarce, we propose to leverage on the knowledge learned by networks specialized in stereo or ﬂow, for which much more data are available, to distill proxy annotations. Exhaustive experiments show that i) DWARF runs at about 10 FPS on a single high-end GPU and about 1 FPS on NVIDIA Jetson TX2 embedded at KITTI resolution, with moderate drop in accuracy compared to 10× deeper models, ii) learning from many distilled samples is more effective than from the few, annotated ones available.

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ICRA Conference 2020 Conference Paper

Real-Time Semantic Stereo Matching

Pier Luigi Dovesi
Matteo Poggi
Lorenzo Andraghetti
Miquel Martí
Hedvig Kjellström
Alessandro Pieropan
Stefano Mattoccia

Scene understanding is paramount in robotics, self-navigation, augmented reality, and many other fields. To fully accomplish this task, an autonomous agent has to infer the 3D structure of the sensed scene (to know where it looks at) and its content (to know what it sees). To tackle the two tasks, deep neural networks trained to infer semantic segmentation and depth from stereo images are often the preferred choices. Specifically, Semantic Stereo Matching can be tackled by either standalone models trained for the two tasks independently or joint end-to-end architectures. Nonetheless, as proposed so far, both solutions are inefficient because requiring two forward passes in the former case or due to the complexity of a single network in the latter, although jointly tackling both tasks is usually beneficial in terms of accuracy. In this paper, we propose a single compact and lightweight architecture for real-time semantic stereo matching. Our framework relies on coarse-to-fine estimations in a multi-stage fashion, allowing: i) very fast inference even on embedded devices, with marginal drops in accuracy, compared to state-of-the-art networks, ii) trade accuracy for speed, according to the specific application requirements. Experimental results on high-end GPUs as well as on an embedded Jetson TX2 confirm the superiority of semantic stereo matching compared to standalone tasks and highlight the versatility of our framework on any hardware and for any application.

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IROS Conference 2018 Conference Paper

Towards Real-Time Unsupervised Monocular Depth Estimation on CPU

Matteo Poggi
Filippo Aleotti
Fabio Tosi
Stefano Mattoccia

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 results. However, these architectures are extremely deep and complex. Thus, real-time performance can be achieved only by leveraging power-hungry GPUs that do not allow to infer depth maps in application fields characterized by low-power constraints. To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image. Similarly to state-of-the-art, we train our network in an unsupervised manner casting depth estimation as an image reconstruction problem. Extensive experimental results on the KITTI dataset show that compared to the top performing approach our network has similar accuracy but a much lower complexity (about 6% of parameters) enabling to infer a depth map for a KITTI image in about 1. 7 s on the Raspberry Pi 3 and at more than 8 Hz on a standard CPU. Moreover, by trading accuracy for efficiency, our network allows to infer maps at about 2 Hz and 40 Hz respectively, still being more accurate than most state-of-the-art slower methods. To the best of our knowledge, it is the first method enabling such performance on CPUs paving the way for effective deployment of unsupervised monocular depth estimation even on embedded systems.

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