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EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images

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arXiv 2023年

作者： Ye, Zhichao Bao, Chong Zhou, Xin Liu, Haomin Bao, Hujun Zhang, Guofeng State Key Laboratory of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou310058 China SenseTime Research Hangzhou311215 China ZJU-SenseTime Joint Lab of 3D Vision China

Structure-from-Motion is a technology used to obtain scene structure through image collection, which is a fundamental problem in computer vision. For unordered Internet images, SfM is very slow due to the lack of prior knowledge about image overlap. For sequential images, knowing the large overlap between adjacent frames, SfM can adopt a variety of acceleration strategies, which are only applicable to sequential data. To further improve the reconstruction efficiency and break the gap of strategies between these two kinds of data, this paper presents an efficient covisibility-based incremental SfM. Different from previous methods, we exploit covisibility and registration dependency to describe the image connection which is suitable to any kind of data. Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two. Experiments on the unordered images and mixed data verify the effectiveness of the proposed method, which is three times faster than the state-of-the-art on feature matching, and an order of magnitude faster on reconstruction without sacrificing the accuracy. The source code is publicly available at https://***/openxrlab/xrsfm. © 2023, CC BY.

关键词： HTTP

Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

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arXiv 2023年

作者： Leitner, Stefan Mirza, M. Jehanzeb Lin, Wei Micorek, Jakub Masana, Marc Kozinski, Mateusz Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Austria TU Graz SAL Dependable Embedded Systems Lab Silicon Austria Labs Austria

关键词： Object detection

Wide-depth-range 6D object pose estimation in space

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arXiv 2021年

作者： Hu, Yinlin Speierer, Sébastien Jakob, Wenzel Fua, Pascal Salzmann, Mathieu EPFL Computer Vision Lab EPFL Realistic Graphics Lab ClearSpace SA

6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric scattering, allowing objects to be visible from a great distance while complicating illumination conditions. Currently available benchmark datasets do not place a sufficient emphasis on this aspect and mostly depict the target in close proximity. Prior work tackling pose estimation under large scale variations relies on a two-stage approach to first estimate scale, followed by pose estimation on a resized image patch. We instead propose a single-stage hierarchical end-to-end trainable network that is more robust to scale variations. We demonstrate that it outperforms existing approaches not only on images synthesized to resemble images taken in space but also on standard benchmarks. © 2021, CC BY.

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CAMERA CALIBRATION THROUGH CAMERA PROJECTION LOSS

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arXiv 2021年

作者： Butt, Talha Hanif Taj, Murtaza Computer Vision and Graphics Lab Lahore University of Management Sciences Lahore Pakistan

Camera calibration is a necessity in various tasks including 3D reconstruction, hand-eye coordination for a robotic interaction, autonomous driving, etc. In this work we propose a novel method to predict extrinsic (baseline, pitch, and translation), intrinsic (focal length and principal point offset) parameters using an image pair. Unlike existing methods, instead of designing an end-to-end solution, we proposed a new representation that incorporates camera model equations as a neural network in a multi-task learning framework. We estimate the desired parameters via novel camera projection loss (CPL) that uses the camera model neural network to reconstruct the 3D points and uses the reconstruction loss to estimate the camera parameters. To the best of our knowledge, ours is the first method to jointly estimate both the intrinsic and extrinsic parameters via a multi-task learning methodology that combines analytical equations in learning framework for the estimation of camera parameters. We also proposed a novel CVGL Camera Calibration dataset using CARLA Simulator [1]. Empirically, we demonstrate that our proposed approach achieves better performance with respect to both deep learning-based and traditional methods on 8 out of 10 parameters evaluated using both synthetic and real data. Our code and generated dataset are available at https://***/thanif/Camera-Calibration-through-Camera-Projection-Loss. © 2021, CC BY.

关键词： Image reconstruction

Video Test-Time Adaptation for Action Recognition

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arXiv 2022年

作者： Lin, Wei Mirza, Muhammad Jehanzeb Kozinski, Mateusz Possegger, Horst Kuehne, Hilde Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Christian Doppler Laboratory for Embedded Machine Learning Goethe University Frankfurt Germany MIT-IBM Watson AI Lab United States

Although action recognition systems can achieve top performance when evaluated on in-distribution test points, they are vulnerable to unanticipated distribution shifts in test data. However, test-time adaptation of video action recognition models against common distribution shifts has so far not been demonstrated. We propose to address this problem with an approach tailored to spatio-temporal models that is capable of adaptation on a single video sample at a step. It consists in a feature distribution alignment technique that aligns online estimates of test set statistics towards the training statistics. We further enforce prediction consistency over temporally augmented views of the same test video sample. Evaluations on three benchmark action recognition datasets show that our proposed technique is architecture-agnostic and able to significantly boost the performance on both, the state of the art convolutional architecture TANet and the Video Swin Transformer. Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts. Code will be available at https://***/wlin-at/ViTTA. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images

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arXiv 2024年

作者： Jin, Yuan Ma, Gege Chen, Geng Lyu, Tianling Egger, Jan Lyu, Junhui Zhang, Shaoting Zhu, Wentao Zhejiang Lab 311121 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China The Zhejiang University School of Medicine Sir Run Run Shaw Hospital Hangzhou310016 China Shanghai Artificial Intelligence Laboratory Shanghai200120 China

The accurate diagnosis of pathological subtypes of lung cancer is of paramount importance for follow-up treatments and prognosis managements. Assessment methods utilizing deep learning technologies have introduced novel approaches for clinical diagnosis. However, the majority of existing models rely solely on single-modality image input, leading to limited diagnostic accuracy. To this end, we propose a novel deep learning network designed to accurately classify lung cancer subtype with multi-dimensional and multi-modality images, i.e., CT and pathological images. The strength of the proposed model lies in its ability to dynamically process both paired CT-pathological image sets as well as independent CT image sets, and consequently optimize the pathology-related feature extractions from CT images. This adaptive learning approach enhances the flexibility in processing multi-dimensional and multi-modality datasets and results in performance elevating in the model testing phase. We also develop a contrastive constraint module, which quantitatively maps the cross-modality associations through network training, and thereby helps to explore the "gold standard" pathological information from the corresponding CT scans. To evaluate the effectiveness, adaptability, and generalization ability of our model, we conducted extensive experiments on a large-scale multi-center dataset and compared our model with a series of state-of-the-art classification models. The experimental results demonstrated the superiority of our model for lung cancer subtype classification, showcasing significant improvements in accuracy metrics such as ACC, AUC, and F1-score. Copyright © 2024, The Authors. All rights reserved.

关键词： Lung cancer

NTIRE 2023 Challenge on Night Photography Rendering

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NTIRE 2023 Challenge on Night Photography Rendering

2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Shutova, Alina Ershov, Egor Perevozchikov, Georgy Ermakov, Ivan Banić, Nikola Timofte, Radu Collins, Richard Efimova, Maria Terekhin, Arseniy Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Lei, Chunxia Wang, Tingniao Wang, Song Liu, Shuai Feng, Chaoyu Shao, Guangqi Wang, Hao Wang, Xiaotao Lei, Lei Xu, Lu Zhang, Chao Wang, Yasi Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Kinli, Furkan Özcan, Bariş Kiraç, Furkan Chung, Hyerin Lee, Nakyung Kwak, Sung Keun Conde, Marcos Elezabi, Omar Seizinger, Tim Vasluianu, Florin Hsieh, Chia-Hsuan Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Chen, I-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Kuo, Sy-Yen Russia Gideon Brothers Croatia Swiss Federal Institute of Technology in Zurich Switzerland University of Milano - Bicocca Italy Zhejiang Dahua Technology China Xiaomi Inc. China Beijing Samsung Teleconmmunication R&D Center China Vision and Graphics Lab Özyeǧin University Turkey Fishency Innovation Norway T-Fashion Inc. Turkey CJ OliveNetworks AI Research Korea Republic of University of Wuerzburg Computer Vision Lab CAIDAS Germany ServiceNow United States Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan University of Würzburg Germany

ISBN: (纸本)9798350302493

This paper presents a review of the NTIRE 2023 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, participants were not provided with a large training dataset for the target sensor. Instead, this time they were given images of a color checker illuminated by a known light source. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. The highest ranking solutions were further ranked by Richard Collins, a renowned photographer. The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://***/ © 2023 IEEE.

关键词： Photography

An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions

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arXiv 2022年

作者： Mirza, Muhammad Jehanzeb Masana, Marc Possegger, Horst Bischof, Horst Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria TU Graz SAL Dependable Embedded Systems Lab Silicon Austria Labs Austria

Although deep neural networks enable impressive visual perception performance for autonomous driving, their robustness to varying weather conditions still requires attention. When adapting these models for changed environments, such as different weather conditions, they are prone to forgetting previously learned information. This catastrophic forgetting is typically addressed via incremental learning approaches which usually re-train the model by either keeping a memory bank of training samples or keeping a copy of the entire model or model parameters for each scenario. While these approaches show impressive results, they can be prone to scalability issues and their applicability for autonomous driving in all weather conditions has not been shown. In this paper we propose DISC - Domain Incremental through Statistical Correction - a simple online zero-forgetting approach which can incrementally learn new tasks (i.e. weather conditions) without requiring re-training or expensive memory banks. The only information we store for each task are the statistical parameters as we categorize each domain by the change in first and second order statistics. Thus, as each task arrives, we simply 'plug and play' the statistical vectors for the corresponding task into the model and it immediately starts to perform well on that task. We show the efficacy of our approach by testing it for object detection in a challenging domain-incremental autonomous driving scenario where we encounter different adverse weather conditions, such as heavy rain, fog, and snow. © 2022, CC BY.

关键词： Meteorology

Probabilistic Word Embeddings in Kinematic Space

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Probabilistic Word Embeddings in Kinematic Space

International Conference on Pattern Recognition

作者： Adarsh Jamadandi Rishabh Tigadoli Ramesh Tabib Uma Mudenagudi Computer Vision and Graphics Lab KLE Technological University Hubli India Mercedes-Benz R and D Centre Bangalore India

In this paper, we propose a method for learning representations in the space of Gaussian-like distribution defined on a novel geometrical space called Kinematic space. The utility of non-Euclidean geometry for deep representation learning has recently been in vogue, specifically models of hyperbolic geometry such as Poincaré and Lorentz models have proven useful for learning hierarchical representations. Going beyond manifolds with constant curvature, albeit has better representation capacity might lead to unhanding of computationally tractable tools like Riemannian optimization methods. Here, we explore a pseudo-Riemannian auxiliary Lorentzian space called Kinematic space and provide a principled approach for constructing a Gaussian-like distribution, which is compatible with gradient-based learning methods, to formulate a probabilistic word embedding framework. Contrary to, mapping lexically distributed representations to a single point vector in Euclidean space, we advocate for mapping entities to density-based representations, as it provides explicit control over the uncertainty in representations. We test our framework by embedding WordNet-Noun hierarchy, a large lexical database, our experiments report strong consistent improvements in Mean Rank and Mean Average Precision (MAP) values compared to probabilistic word embedding frameworks defined on Euclidean and hyperbolic spaces. We show an average improvement of 72.68% in MAP and 82.60% in Rank compared to the hyperbolic version. Our work serves as evidence for the utility of novel geometrical spaces for learning hierarchical representations.

关键词： Geometry Uncertainty Computational modeling Kinematics Transforms Aerospace electronics Tools