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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez;Fabio Tosi;Luigi Di Stefano;Radu Timofte;Alex Costanzino;Matteo Poggi;Samuele Salti;Stefano Mattoccia;Yangyang Zhang;Cailin Wu;Zhuangda He;Shuangshuang Yin;Jiaxu Dong;Yangchenxu Liu;Hao Jiang;Jun Shi;Yong A;Yixiang Jin;Dingzhe Li;Bingxin Ke;Anton Obukhov;Tinafu Wang;Nando Metzger;Shengyu Huang;Konrad Schindler;Yachuan Huang;Jiaqi Li;Junrui Zhang;Yiran Wang;Zihao Huang;Tianqi Liu;Zhiguo Cao;Pengzhi Li;Jui-Lin Wang;Wenjie Zhu;Hui Geng;Yuxin Zhang;Long Lan;Kele Xu;Tao Sun;Qisheng Xu;Sourav Saini;Aashray Gupta;Sahaj K. Mistry;Aryan Shukla;Vinit Jakhetiya;Sunil Jaiswal;Yuejin Sun;Zhuofan Zheng;Yi Ning;Jen-Hao Cheng;Hou-I Liu;Hsiang-Wei Huang;Cheng-Yen Yang;Zhongyu Jiang;Yi-Hao Peng;Aishi Huang;Jenq-Neng Hwang
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (16)
This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The cha...Show More
NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez;Fabio Tosi;Luigi Di Stefano;Radu Timofte;Alex Costanzino;Matteo Poggi;Samuele Salti;Stefano Mattoccia;Yangyang Zhang;Cailin Wu;Zhuangda He;Shuangshuang Yin;Jiaxu Dong;Yangchenxu Liu;Hao Jiang;Jun Shi;Yong A;Yixiang Jin;Dingzhe Li;Bingxin Ke;Anton Obukhov;Tinafu Wang;Nando Metzger;Shengyu Huang;Konrad Schindler;Yachuan Huang;Jiaqi Li;Junrui Zhang;Yiran Wang;Zihao Huang;Tianqi Liu;Zhiguo Cao;Pengzhi Li;Jui-Lin Wang;Wenjie Zhu;Hui Geng;Yuxin Zhang;Long Lan;Kele Xu;Tao Sun;Qisheng Xu;Sourav Saini;Aashray Gupta;Sahaj K. Mistry;Aryan Shukla;Vinit Jakhetiya;Sunil Jaiswal;Yuejin Sun;Zhuofan Zheng;Yi Ning;Jen-Hao Cheng;Hou-I Liu;Hsiang-Wei Huang;Cheng-Yen Yang;Zhongyu Jiang;Yi-Hao Peng;Aishi Huang;Jenq-Neng Hwang
Year: 2024 | Conference Paper |
Test Time Training for Industrial Anomaly Segmentation
Alex Costanzino;Pierluigi Zama Ramirez;Mirko Del Moro;Agostino Aiezzo;Giuseppe Lisanti;Samuele Salti;Luigi Di Stefano
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality control. While existing methods excel in generating anomaly scores for each pixel, practical applications require producing a binary segmentation to identify anomalies. Due to the absence of labeled anomalies in many real scenarios, standard practices binarize these maps based on some statistics derived from a validation s...Show More
Connecting NeRFs, Images, and Text
Francesco Ballerini;Pierluigi Zama Ramirez;Roberto Mirabella;Samuele Salti;Luigi Di Stefano
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, sim...Show More
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
Alex Costanzino;Pierluigi Zama Ramirez;Giuseppe Lisanti;Luigi Di Stefano
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (5)
Recent advancements have shown the potential of lever-aging both point clouds and images to localize anomalies. Nevertheless, their applicability in industrial manufacturing is often constrained by significant drawbacks, such as the use of memory banks, which leads to a substantial in-crease in terms of memory footprint and inference times. We propose a novel light and fast framework that learns t...Show More
Learning Depth Estimation for Transparent and Mirror Surfaces
Alex Costanzino;Pierluigi Zama Ramirez;Matteo Poggi;Fabio Tosi;Stefano Mattoccia;Luigi Di Stefano
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (8)
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 to estimate depth properly for such surfaces with neural networks, without requiring any ground-truth annotation. We unveil how to obtain reliable pseudo labels by in-painting ToM objects in images and processing them wi...Show More
Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation
Adriano Cardace;Pierluigi Zama Ramirez;Samuele Salti;Luigi Di Stefano
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality. However, the task is extremely challenging due to ambiguities coming from the unstructured, sparse, and uncolored nature of the 3D point clouds. A possible solution is to combine the 3D information with others coming from sensors featuring a different modality, such...Show More
Boosting Multi-Modal Unsupervised Domain Adaptation for LiDAR Semantic Segmentation by Self-Supervised Depth Completion
Adriano Cardace;Andrea Conti;Pierluigi Zama Ramirez;Riccardo Spezialetti;Samuele Salti;Luigi Di Stefano
LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which however suffer from the domain shift problem as other deep learning approaches. To address this, we propose a novel Unsupervised Domain Adaptation (UDA) tec...Show More
ScanNeRF: a Scalable Benchmark for Neural Radiance Fields
Luca De Luigi;Damiano Bolognini;Federico Domeniconi;Daniele De Gregorio;Matteo Poggi;Luigi Di Stefano
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and implement an effective pipeline for scanning real objects in quantity and effortlessly. Our scan station is built with less than 500$ hardware budget and can collect roughly 4000 images of a scanned object in just 5 minutes. ...Show More
Self-Distillation for Unsupervised 3D Domain Adaptation
Adriano Cardace;Riccardo Spezialetti;Pierluigi Zama Ramirez;Samuele Salti;Luigi Di Stefano
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2023 | Conference Paper |
Cited by: Papers (11)
Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as those at training time. Unsupervised Domain Adaptation (UDA) instead, breaks this assumption and tries to solve the task on an unlabeled target domain, leveraging only on a supervised source domain. For point cloud class...Show More
Learning Good Features to Transfer Across Tasks and Domains
Pierluigi Zama Ramirez;Adriano Cardace;Luca De Luigi;Alessio Tonioni;Samuele Salti;Luigi Di Stefano
Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to solve different tasks share the same architecture suggests that there should be a way of reusing the knowledge learned in a specific setting to solve novel tasks with limited or no additional supervision. In this work, ...Show More
Cross-Spectral Neural Radiance Fields
Matteo Poggi;Pierluigi Zama Ramirez;Fabio Tosi;Samuele Salti;Stefano Mattoccia;Luigi Di Stefano
2022 International Conference on 3D Vision (3DV)
Year: 2022 | Conference Paper |
Cited by: Papers (10)
We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation. X-NeRF optimizes camera poses across spectra during training and exploits Normalized Cross-Device Coordinates (NXDC) to render images of different modalities from arbitrary viewpoints, which a...Show More
Unsupervised Learning of Local Equivariant Descriptors for Point Clouds
Marlon Marcon;Riccardo Spezialetti;Samuele Salti;Luciano Silva;Luigi Di Stefano
Correspondences between 3D keypoints generated by matching local descriptors are a key step in 3D computer vision and graphic applications. Learned descriptors are rapidly evolving and outperforming the classical handcrafted approaches in the field. Yet, to learn effective representations they require supervision through labeled data, which are cumbersome and time-consuming to obtain. Unsupervised...Show More
Continual Adaptation for Deep Stereo
Matteo Poggi;Alessio Tonioni;Fabio Tosi;Stefano Mattoccia;Luigi Di Stefano
Depth estimation from stereo images is carried out with unmatched results by convolutional neural networks trained end-to-end to regress dense disparities. Like for most tasks, this is possible if large amounts of labelled samples are available for training, possibly covering the whole data distribution encountered at deployment time. Being such an assumption systematically unmet in real applicati...Show More
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
Adriano Cardace;Riccardo Spezialetti;Pierluigi Zama Ramirez;Samuele Salti;Luigi Di Stefano
2021 International Conference on 3D Vision (3DV)
Year: 2021 | Conference Paper |
Cited by: Papers (8)
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align features across domains has been the standard way to tackle it. In this paper, we take a different path and propose RefRec, the first approach to investigate pseudo-labels and self-training in UDA for point clouds. We pres...Show More
Shooting Labels: 3D Semantic Labeling by Virtual Reality
Pierluigi Zama Ramirez;Claudio Paternesi;Luca De Luigi;Luigi Lella;Daniele De Gregorio;Luigi Di Stefano
2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
Year: 2020 | Conference Paper |
Cited by: Papers (12)
Availability of a few, large-size, annotated datasets, like ImageNet, Pascal VOC and COCO, has lead deep learning to revolutionize computer vision research by achieving astonishing results in several vision tasks. We argue that new tools to facilitate generation of annotated datasets may help spreading data-driven AI throughout applications and domains. In this work we propose Shooting Labels, the...Show More
Ambiguity in Sequential Data: Predicting Uncertain Futures With Recurrent Models
Alessandro Berlati;Oliver Scheel;Luigi Di Stefano;Federico Tombari
Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data, which is of special importance, as often multiple futures are equally likely. Our approach can ...Show More
Unsupervised Domain Adaptation for Depth Prediction from Images
Alessio Tonioni;Matteo Poggi;Stefano Mattoccia;Luigi Di Stefano
State-of-the-art approaches to infer dense depth measurements from images rely on CNNs trained end-to-end on a vast amount of data. However, these approaches suffer a drastic drop in accuracy when dealing with environments much different in appearance and/or context from those observed at training time. This domain shift issue is usually addressed by fine-tuning on smaller sets of images from the ...Show More
Real-Time RGB-D Camera Pose Estimation in Novel Scenes Using a Relocalisation Cascade
Tommaso Cavallari;Stuart Golodetz;Nicholas A. Lord;Julien Valentin;Victor A. Prisacariu;Luigi Di Stefano;Philip H. S. Torr
Camera pose estimation is an important problem in computer vision, with applications as diverse as simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques match the current image against keyframes with known poses coming from a tracker, directly regress the pose, or establish correspondences between keypoints in the current image and points in the scene i...Show More
Learning an Effective Equivariant 3D Descriptor Without Supervision
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (24)
Establishing correspondences between 3D shapes is a fundamental task in 3D Computer Vision, typically ad- dressed by matching local descriptors. Recently, a few at- tempts at applying the deep learning paradigm to the task have shown promising results. Yet, the only explored way to learn rotation invariant descriptors has been to feed neural networks with highly engineered and invariant representa...Show More
Learning Across Tasks and Domains
Pierluigi Zama Ramirez;Alessio Tonioni;Samuele Salti;Luigi Di Stefano
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (27)
Recent works have proven that many relevant visual tasks are closely related one to another. Yet, this connection is seldom deployed in practice due to the lack of practical methodologies to transfer learned concepts across different training processes. In this work, we introduce a novel adaptation framework that can operate across both task and domains. Our framework learns to transfer knowledge ...Show More
GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching
Simone Melzi;Riccardo Spezialetti;Federico Tombari;Michael M. Bronstein;Luigi Di Stefano;Emanuele Rodolà
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (12)
We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes o...Show More
Real-Time Self-Adaptive Deep Stereo
Alessio Tonioni;Fabio Tosi;Matteo Poggi;Stefano Mattoccia;Luigi Di Stefano
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (183)
Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set (e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective...Show More
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Tommaso Cavallari;Stuart Golodetz;Nicholas A. Lord;Julien Valentin;Luigi Di Stefano;Philip H. S. Torr
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017 | Conference Paper |
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques either match the current image against keyframes with known poses coming from a tracker, or establish 2D-to-3D correspondences between keypoints in the current image and points in the scene in order to estimate the...Show More
A Global Hypothesis Verification Framework for 3D Object Recognition in Clutter
Aitor Aldoma;Federico Tombari;Luigi Di Stefano;Markus Vincze
Pipelines to recognize 3D objects despite clutter and occlusions usually end up with a final verification stage whereby recognition hypotheses are validated or dismissed based on how well they explain sensor measurements. Unlike previous work, we propose a Global Hypothesis Verification (GHV) approach which regards all hypotheses jointly so as to account for mutual interactions. GHV provides a pri...Show More
Learning a Descriptor-Specific 3D Keypoint Detector
Samuele Salti;Federico Tombari;Riccardo Spezialetti;Luigi Di Stefano
2015 IEEE International Conference on Computer Vision (ICCV)
Year: 2015 | Conference Paper |
Keypoint detection represents the first stage in the majority of modern computer vision pipelines based on automatically established correspondences between local descriptors. However, no standard solution has emerged yet in the case of 3D data such as point clouds or meshes, which exhibit high variability in level of detail and noise. More importantly, existing proposals for 3D keypoint detection...Show More
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