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IEEE Annual International Symposium Virtual Reality

作者： Duotun Wang James Kubrlcht Yixin Zhu Wei Lianq Song-Chun Zhu Chenfanfu Jiang Hongjing Lu Laboratory of Intelligent Information Technology Beijing Institute of Technology UCLA Computational Vision and Learning Laboratory Cognition Center for Vision Computer Graphics Group UPenn

ISBN: (纸本)9781538633663

When a moving object collides with an object at rest, people immediately perceive a causal event: i.e., the first object has launched the second object forwards. However, when the second object's motion is delayed, or is accompanied by a collision sound, causal impressions attenuate and strengthen. Despite a rich literature on causal perception, researchers have exclusively utilized 2D visual displays to examine the launching effect. It remains unclear whether people are equally sensitive to the spatiotemporal properties of observed collisions in the real world. The present study first examined whether previous findings in causal perception with audiovisual inputs can be extended to immersive 3D virtual environments. We then investigated whether perceived causality is influenced by variations in the spatial position of an auditory collision indicator. We found that people are able to localize sound positions based on auditory inputs in VR environments, and spatial discrepancy between the estimated position of the collision sound and the visually observed impact location attenuates perceived causality.

关键词： Three-dimensional displays Visualization Solid modeling Delays Spatiotemporal phenomena Virtual environments

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Enforcing connectivity of 3D linear structures using their 2D projections

arXiv

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arXiv 2022年

作者： Oner, Doruk Osman, Hussein Kozinski, Mateusz Fua, Pascal Computer Vision Laboratory École Polytechnique Fédérale de Lausanne Switzerland Institute of Computer Graphics and Vision Graz University of Technology Austria

Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture the topological properties of these structures. As a result, the connectivity of the recovered structures is often wrong, which lessens their usefulness. In this paper, we propose to improve the 3D connectivity of our results by minimizing a sum of topology-aware losses on their 2D projections. This suffices to increase the accuracy and to reduce the annotation effort required to provide the required annotated training data. Code is available at https://***/doruk-oner/ConnectivityOnProjections. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images

A Novel Representation of Parts for Accurate 3D Object Detec...

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International Conference on computer vision (ICCV)

作者： Alberto Crivellaro Mahdi Rad Yannick Verdie Kwang Moo Yi Pascal Fua Vincent Lepetit Computer Vision Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland Institute for Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9781467383929

We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object. Our method relies only on grayscale images since depth cameras fail on metallic objects, it can handle poorly textured objects, and cluttered, changing environments, the pose it predicts degrades gracefully in presence of large occlusions. As a result, by contrast with the state-of-the-art, our method is suitable for practical Augmented Reality applications even in industrial environments. To be robust to occlusions, we first learn to detect some parts of the target object. Our key idea is to then predict the 3D pose of each part in the form of the 2D projections of a few control points. The advantages of this representation is three-fold: We can predict the 3D pose of the object even when only one part is visible, when several parts are visible, we can combine them easily to compute a better pose of the object, the 3D pose we obtain is usually very accurate, even when only few parts are visible.

关键词： Three-dimensional displays Cameras Robustness Training Object detection Augmented reality Image edge detection

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Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures 15th

Depth-Awareness in a System for Mixed-Reality Aided Surgical...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Sylos Labini, Mauro Gsaxner, Christina Pepe, Antonio Wallner, Juergen Egger, Jan Bevilacqua, Vitoantonio Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030267650

computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications. © 2019, Springer Nature Switzerland AG.

关键词： Mixed reality

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Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars

arXiv

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arXiv 2017年

作者： Jiang, Chenfanfu Qi, Siyuan Zhu, Yixin Huang, Siyuan Lin, Jenny Yu, Lap-Fai Terzopoulos, Demetri Zhu, Song-Chun SIG Center for Computer Graphics University of Pennsylvania UCLA Center for Vision Cognition Learning and Autonomy University of California Los Angeles United States Graphics and Virtual Environments Laboratory University of Massachusetts Boston UCLA Computer Graphics & Vision Laboratory University of California Los Angeles United States

We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms. In particular, we devise a learning-based pipeline of algorithms capable of automatically generating and rendering a potentially infinite variety of indoor scenes by using a stochastic grammar, represented as an attributed Spatial And-Or Graph, in conjunction with state-of-the-art physics-based rendering. Our pipeline is capable of synthesizing scene layouts with high diversity, and it is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. It renders photorealistic RGB images of the generated scenes while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity, and material information (detailed to object parts), as well as environments (e.g., illuminations and camera viewpoints). We demonstrate the value of our synthesized dataset, by improving performance in certain machine-learning-based scene understanding tasks—depth and surface normal prediction, semantic segmentation, reconstruction, etc.—and by providing benchmarks for and diagnostics of trained models by modifying object attributes and scene properties in a controllable manner. Copyright © 2017, The Authors. All rights reserved.

关键词： Pixels

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3D roof details by 3D aerial vision

3D roof details by 3D aerial vision

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Philipp Meixner Franz Leberl Mathieu Brédif Institute for Computer Graphics and Vision Graz University of Technology Austria MATIS Laboratory IGN Paris France

Virtual cities in a 3D GIS will not be complete without the details of roofs with chimneys, dormer windows, skylights. We contribute a method of extracting such details from digital large format aerial photography. The dense point clouds produced by a photogrammetric process produce major and minor plane surfaces of a roof and enter into a classification of its superstructures. In real city data with a total of 1312 superstructures the proposed automated process finds 1024, thus achieving success at a rate of 78%.

关键词： Buildings Three dimensional displays Image edge detection Cities and towns Smoothing methods Geographic Information Systems Photography

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Partial generalized correlation for hyperspectral data

Partial generalized correlation for hyperspectral data

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Marc Strickert Björn Labitzke Volker Blanz Institute of Vision and Graphics University of Siegen Germany Computer Graphics Group University of Siegen Germany Media Systems Laboratory University of Siegen Germany

A variational approach is proposed for the unsupervised assessment of attribute variability of high-dimensional data given a differentiable similarity measure. The key question addressed is how much each data attribute contributes to an optimum transformation of vectors for reaching maximum similarity. This question is formalized and solved in a mathematically rigorous optimization framework for each data pair of interest. Trivially, for the Euclidean metric minimization to zero distance induces highest vector similarity, but in case of the linear Pearson correlation measure the highest similarity of one is desired. During optimization the not necessarily symmetric trajectories between two vectors are recorded and analyzed in terms of attribute changes and line integral. The proposed formalism allows to assess partial covariance and correlation characteristics of data attributes for vectors being compared by any differentiable similarity measure. Its potential for generating alternative and localized views such as for contrast enhancement is demonstrated for hyperspectral images from the remote sensing domain.

关键词： Correlation Euclidean distance Covariance matrix Vectors Equations Mathematical model Geologic measurements

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FAST3D: Flow-aware self-training for 3D object detectors

arXiv

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arXiv 2021年

作者： Fruhwirth-Reisinger, Christian Opitz, Michael Possegger, Horst Bischof, Horst Christian Doppler Laboratory for Embedded Machine Learning Institute of Computer Graphics and Vision Graz University of Technology

In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors. This eliminates the need for expensive, high-quality labels whenever the environment changes (e.g., geographic location, sensor setup, weather condition). State-of-the-art self-training approaches, however, mostly ignore the temporal nature of autonomous driving data. To address this issue, we propose a flow-aware self-training method which enables unsupervised domain adaptation for 3D object detectors on continuous LiDAR point clouds. In order to get reliable pseudo-labels, we leverage scene flow to propagate detections through time. In particular, we introduce a flow-based multi-target tracker, that exploits flow consistency to filter and refine resulting tracks. The emerged precise pseudo-labels then serve as a basis for model re-training. Starting with a pre-trained KITTI model, we conduct experiments on the challenging Waymo Open Dataset to demonstrate the effectiveness of our approach. Without any prior target domain knowledge, our results show a significant improvement over the state-of-the-art. © 2021, CC BY-NC-SA.

关键词： Object detection

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Simultaneous independent image display technique on multiple 3d objects

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2017年 10114 LNCS卷 440-455页

作者： Hirukawa, Takuto Visentini-Scarzanella, Marco Kawasaki, Hiroshi Furukawa, Ryo Hiura, Shinsaku Computer Vision and Graphics Laboratory Kagoshima University Kagoshima Japan Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

ISBN: (纸本)9783319541891

We propose a new system to visualize depth-dependent patterns and images on solid objects with complex geometry using multiple projectors. The system, despite consisting of conventional passive LCD projectors, is able to project different images and patterns depending on the spatial location of the object. The technique is based on the simple principle that multiple patterns projected from multiple projectors interfere constructively with each other when their patterns are projected on the same object. Previous techniques based on the same principle can only achieve (1) low resolution volume colorization or (2) high resolution images but only on a limited number of flat planes. In this paper, we discretize a 3D object into a number of 3D points so that high resolution images can be projected onto the complex shapes. We also propose a dynamic ranges expansion technique as well as an efficient optimization procedure based on epipolar constraints. Such technique can be used to the extend projection mapping to have spatial dependency, which is desirable for practical applications. We also demonstrate the system potential as a visual instructor for object placement and assembling. Experiments prove the effectiveness of our method. © Springer International Publishing AG 2017.

关键词： Liquid crystal displays

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Incorporating User Provided Constraints into Document Clustering

Incorporating User Provided Constraints into Document Cluste...

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IEEE International Conference on Data Mining (ICDM)

作者： Yanhua Chen Manjeet Rege Ming Dong Jing Hua Department of Computer Science Machine Vision and Pattern Recognition Laboratory Detroit MI USA Department of Computer Science Graphics and Imaging Laboratory Detroit MI USA

Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they "must" or "cannot" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.

关键词： Iterative algorithms Clustering algorithms Symmetric matrices Data mining Databases Clustering methods Couplings Machine vision Pattern recognition computer graphics

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