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检索条件"主题词=3D Computer Vision"

共 243 条记录，以下是151-160 订阅

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TriCoLo: Trimodal Contrastive Loss for Text to Shape Retrieval

TriCoLo: Trimodal Contrastive Loss for Text to Shape Retriev...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Ruan, Yue Lee, Han-Hung Zhang, Yiming Zhang, Ke Chang, Angel X. Simon Fraser Univ Burnaby BC Canada Alberta Machine Intelligence Inst Amii Edmonton AB Canada

ISBN: (纸本)9798350318920;9798350318937

Text-to-shape retrieval is an increasingly relevant problem with the growth of 3d shape data. Recent work on contrastive losses for learning joint embeddings over multimodal data [45] has been successful at tasks such as retrieval and classification. Thus far, work on joint representation learning for 3d shapes and text has focused on improving embeddings through modeling of complex attention between representations [53], or multi-task learning [25]. We propose a trimodal learning scheme over text, multi-view images and 3d shape voxels, and show that with large batch contrastive learning we achieve good performance on text-to-shape retrieval without complex attention mechanisms or losses. Our experiments serve as a foundation for follow-up work on building trimodal embeddings for text-image-shape.

关键词： 3d computer vision Algorithms Algorithms vision + language and/or other modalities

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ZIGNeRF: Zero-shot 3d Scene Representation with Invertible Generative Neural Radiance Fields

ZIGNeRF: Zero-shot 3D Scene Representation with Invertible G...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Ko, Kanghyeok Lee, Minhyeok Chung Ang Univ Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Generative Neural Radiance Fields (NeRFs) have demonstrated remarkable proficiency in synthesizing multi-view images by learning the distribution of a set of unposed images. despite the aptitude of existing Generative NeRFs in generating 3d-consistent high-quality random samples within data distribution, the creation of a 3d representation of a singular input image remains a formidable challenge. In this manuscript, we introduce ZIGNeRF, an innovative model that executes zero-shot Generative Adversarial Network (GAN) inversion for the generation of multi-view images from a single out-of-distribution image. The model is underpinned by a novel inverter that maps out-of-domain images into the latent code of the generator manifold. Notably, ZIGNeRF is capable of disentangling the object from the background and executing 3d operations such as 360-degree rotation or depth and horizontal translation. The efficacy of our model is validated using multiple real-image datasets: Cats, AFHQ, CelebA, CelebA-HQ, and CompCars.

关键词： 3d 3d computer vision Algorithms Algorithms Algorithms Computational photography etc. Generative models for image image and video synthesis video

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ZRG: A dataset for Multimodal 3d Residential Rooftop Understanding

ZRG: A Dataset for Multimodal 3D Residential Rooftop Underst...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Corley, Isaac Lwowski, Jonathan Najafirad, Peyman Univ Texas San Antonio San Antonio TX 78249 USA Zeitview San Antonio TX 78249 USA

ISBN: (纸本)9798350318920;9798350318937

A crucial part of any home is the roof over our heads to protect us from the elements. In this paper we present the Zeitview Rooftop Geometry (ZRG) dataset for residential rooftop understanding. ZRG is a large-scale residential rooftop dataset of over 20k properties collected through roof inspections from across the U.S. and contains multiple modalities including high resolution aerial orthomosaics, digital surface models (dSM), colored point clouds, and 3d roof wireframe annotations. We provide an in-depth analysis and perform several experimental baselines including roof outline extraction, monocular height estimation, and planar roof structure extraction, to illustrate a few of the numerous potential applications unlocked by this dataset.(1)

关键词： 3d computer vision Algorithms Algorithms datasets and evaluations

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SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering

SupeRVol: Super-Resolution Shape and Reflectance Estimation ...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Brahimi, Mohammed Haefner, Bjoern Yenamandra, Tarun Goldluecke, Bastian Cremers, daniel Tech Univ Munich Munich Germany Univ Konstanz Constance Germany Munich Ctr Machine Learning Munich Germany

ISBN: (纸本)9798350318920;9798350318937

We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3d shape and material parameters from a set of color images in a super-resolution manner. To this end, we represent both the bidirectional reflectance distribution function's (BRdF) parameters and the signed distance function (SdF) by multi-layer perceptrons (MLPs). In order to obtain both the surface shape and its reflectance properties, we revert to a differentiable volume renderer with a physically based illumination model that allows us to decouple reflectance and lighting. This physical model takes into account the effect of the camera's point spread function thereby enabling a reconstruction of shape and material in a super-resolution quality. Experimental validation confirms that SupeRVol achieves state of the art performance in terms of inverse rendering quality. It generates reconstructions that are sharper than the individual input images, making this method ideally suited for 3d modeling from low-resolution imagery.

关键词： 3d computer vision Algorithms

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Effects of Markers in Training datasets on the Accuracy of 6d Pose Estimation

Effects of Markers in Training Datasets on the Accuracy of 6...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Rosskamp, Janis Weller, Rene Zachmann, Gabriel Univ Bremen Comp Graph & Virtual Real Bremen Germany

ISBN: (纸本)9798350318920;9798350318937

Collecting training data for pose estimation methods on images is a time-consuming task and usually involves some kind of manual labeling of the 6d pose of objects. This time could be reduced considerably by using marker-based tracking that would allow for automatic labeling of training images. However, images containing markers may reduce the accuracy of pose estimation due to a bias introduced by the markers. In this paper, we analyze the influence of markers in training images on pose estimation accuracy. We investigate the accuracy of estimated poses for three different cases: i) training on images with markers, ii) removing markers by inpainting, and iii) augmenting the dataset with randomly generated markers to reduce spatial learning of marker features. Our results demonstrate that utilizing marker-based techniques is an effective strategy for collecting large amounts of ground truth data for pose prediction. Moreover, our findings suggest that the usage of inpainting techniques do not reduce prediction accuracy. Additionally, we investigate the effect of inaccuracies of labeling in training data on prediction accuracy. We show that the precise ground truth data obtained through marker tracking proves to be superior compared to markerless datasets if labeling errors of 6d ground truth exist. Our data generation tools are available online: https://***/JHRosskamp/6dPosedataGenTools

关键词： 3d computer vision Algorithms Algorithms datasets and evaluations

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Cross-domain Few-Shot Incremental Learning for Point-Cloud Recognition

Cross-Domain Few-Shot Incremental Learning for Point-Cloud R...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Tan, Yuwen Xiang, Xiang Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Sensing 3d objects is critical when 2d object recognition is not accessible. A robot pre-trained on a large point-cloud dataset will encounter unseen classes of 3d objects after deploying it. Therefore, the robot should be able to learn continuously in real-world scenarios. Few-shot class-incremental learning (FSCIL) requires the model to learn from few-shot new examples continually and not forget past classes. However, there is an implicit but strong assumption in the FSCIL that the distribution of the base and incremental classes is the same. In this paper, we focus on cross-domain FSCIL for point-cloud recognition. We decompose the catastrophic forgetting into base class forgetting and incremental class forgetting and alleviate them separately. We utilize the base model to discriminate base samples and new samples by treating base samples as in-distribution samples, and new objects as out-of-distribution samples. We retain the base model to avoid catastrophic forgetting of base classes and train an extra domain-specific module for all new samples to adapt to new classes. At inference, we first discriminate whether the sample belongs to the base class or the new class. Once classified at the model level, test samples are then passed to the corresponding model for class-level classification. To better mitigate the forgetting of new classes, we adopt the soft label and hard label replay together. Extensive experiments on synthetic-to-real incremental 3d datasets show that our proposed method can balance the performance between the base and new objects and outperforms the previous state-of-the-art methods.

关键词： 3d computer vision Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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A Sequential Learning-based Approach for Monocular Human Performance Capture

A Sequential Learning-based Approach for Monocular Human Per...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Chen, Jianchun Vongkulbhisal, Jayakorn Fradel, Fernando de la Torre Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9798350318920;9798350318937

Human performance capture from RGB videos in unconstrained environments has become very popular for applications that require generating virtual avatars or digital actors. SOTA methods use neural network (NN) techniques to estimate the shape directly from photos, yielding a simplified model of the human body. While effective, NN techniques frequently fail under challenging poses and do not preserve temporal consistency. On the other hand, optimization-based methods like shape-from-silhouette can produce more precise reconstruction;however, they typically require a good initialization and are computationally more intensive than NN. To address issues of previous methods, this work proposes a learning-based approach for optimizing fine-grained shape representation from a monocular RGB video. Our main idea is to sequentially recover different shape details (i.e. average shape, clothing, wrinkles) using separate neural networks. At each level, our network takes the sparse/noisy gradients of body mesh vertices w.r.t. the shape, and predicts dense gradients to update the body shape. despite being trained on synthetic data, these networks have surprisingly good generalization to real images. Experimental validation shows that our approach outperforms NN approaches in recovering shape details while also being an order of magnitude faster than optimization-based methods and robust across varied poses and novel views.

关键词： 3d computer vision Algorithms Algorithms Biometrics body pose face gesture

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FIRe: Fast Inverse Rendering using directional and Signed distance Functions

FIRe: Fast Inverse Rendering using Directional and Signed Di...

引用

IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Yenamandra, Tarun Tewari, Ayush Yang, Nan Bernard, Florian Theobalt, Christian Cremers, daniel Tech Univ Munich MCML Munich Germany MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Bonn Bonn Germany MPI Informat SIC Frankfurt Germany

ISBN: (纸本)9798350318920;9798350318937

Neural 3d implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3d reconstruction. A major downside of existing approaches while rendering an image is that they require evaluating the network multiple times per camera ray so that the high computational time forms a bottleneck for downstream applications. We address this problem by introducing a novel neural scene representation that we call the directional distance function (ddF). To this end, we learn a signed distance function (SdF) along with our ddF model to represent a class of shapes. Specifically, our ddF is defined on the unit sphere and predicts the distance to the surface along any given direction. Therefore, our ddF allows rendering images with just a single network evaluation per camera ray. Based on our ddF, we present a novel fast algorithm (FIRe) to reconstruct 3d shapes given a posed depth map. We evaluate our proposed method on 3d reconstruction from single-view depth images, where we empirically show that our algorithm reconstructs 3d shapes more accurately and it is more than 15 times faster (per iteration) than competing methods.

关键词： 3d computer vision Algorithms

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Performance Evaluation of Learned 3d Features 20th

Performance Evaluation of Learned 3D Features

引用

20th International Conference on Image Analysis and Processing (ICIAP)

作者： Spezialetti, Riccardo Salti, Samuele di Stefano, Luigi Viale Risorgimento 2 Bologna Italy

ISBN: (纸本)9783030306427;9783030306410

Matching surfaces is a challenging 3d computer vision problem typically addressed by local features. Although a variety of 3d feature detectors and descriptors has been proposed in literature, they have seldom been proposed together and it is yet not clear how to identify the most effective detector-descriptor pair for a specific application. A promising solution is to leverage machine learning to learn the optimal 3d detector for any given 3d descriptor [15]. In this paper, we report a performance evaluation of the detector-descriptor pairs obtained by learning a paired 3d detector for the most popular 3d descriptors. In particular, we address experimental settings dealing with object recognition and surface registration.

关键词： 3d computer vision Surface matching 3d features

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Registered and Segmented deformable Object Reconstruction from a Single View Point Cloud

Registered and Segmented Deformable Object Reconstruction fr...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Henrichi, Pit Gyenes, Balazs Scheikli, Paul Maria Neumann, Gerhard Mathis-Ullrich, Franziska FAU Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany

ISBN: (纸本)9798350318920;9798350318937

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a system that can recognize and locate these segments in sensor data of deformed real world objects. This is normally done using deformable object registration, which is problem specific and complex to tune. Recent methods utilize neural occupancy functions to improve deformable object registration by registering to an object reconstruction. Going one step further, we propose a system that in addition to reconstruction learns segmentation of the reconstructed object. As the resulting output already contains the information about the segments, we can skip the registration process. Tested on a variety of deformable objects in simulation and the real world, we demonstrate that our method learns to robustly find these segments. We also introduce a simple sampling algorithm to generate better training data for occupancy learning.

关键词： 3d computer vision Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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