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检索条件"主题词=3D Computer Vision"

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Hd-Fusion: detailed Text-to-3d Generation Leveraging Multiple Noise Estimation

HD-Fusion: Detailed Text-to-3D Generation Leveraging Multipl...

引用

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

作者： Wu, Jinbo Gao, Xiaobo Liu, Xing Shen, Zhengyang Zhao, Chen Feng, Haocheng Liu, Jingtuo ding, Errui Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

In this paper, we study Text-to-3d content generation leveraging 2d diffusion priors to enhance the quality and detail of the generated 3d models. Recent progress [11] in text-to-3d has shown that employing high-resolution (e.g., 512 x 512) renderings can lead to the production of high-quality 3d models using latent diffusion priors. To enable rendering at even higher resolutions, which has the potential to further augment the quality and detail of the models, we propose a novel approach that combines multiple noise estimation processes with a pretrained 2d diffusion prior. distinct from the Bar-Tal et al.s' study which binds multiple denoised results [1] to generate images from texts, our approach integrates the computation of scoring distillation losses such as SdS loss and VSd loss which are essential techniques for the 3d content generation with 2d diffusion priors. We experimentally evaluated the proposed approach. The results show that the proposed approach can generate high-quality details compared to the baselines.

关键词： 3d 3d computer vision Algorithms Algorithms etc Generative models for image video

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LensNeRF: Rethinking Volume Rendering based on Thin-Lens Camera Model

LensNeRF: Rethinking Volume Rendering based on Thin-Lens Cam...

引用

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

作者： Kim, Min-Jung Gu, Gyojung Choo, Jaegul Korea Adv Inst Sci & Technol Grad Sch AI Daejeon South Korea

ISBN: (纸本)9798350318920;9798350318937

Recent advances in Neural Radiance Field (NeRF) show promising results in rendering realistic novel view images. However, NeRF and its variants assume that input images are captured using a pinhole camera and that subjects in images are always all-in-focus by tacit agreement. In this paper, we propose aperture-aware NeRF optimization and rendering methods using a thin-lens model (dubbed LensNeRF), which allows defocus images of any aperture size as input and output. To generalize a pinhole camera model to a thin-lens camera model in NeRF framework, we define multiple rays originating from the aperture area, solving world-to-pixel scale ambiguity. Also, we propose in-focus loss that assigns the given pixel color to points on the focus plane to alleviate the color ambiguity caused by the use of multiple rays. For the rigorous evaluation of the proposed method, we collect a real forward-facing dataset with different F-numbers for each viewpoint. Experimental results demonstrate that our method successfully fuses an aperture-size adjustable thin-lens camera model into the NeRF architecture, showing favorable qualitative and quantitative results compared to baseline models. The dataset will be made available.

关键词： 3d computer vision Algorithms Algorithms Computational photography image and video synthesis

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SOAP: Cross-sensor domain Adaptation for 3d Object detection Using Stationary Object Aggregation Pseudo-labelling

SOAP: Cross-sensor Domain Adaptation for 3D Object Detection...

引用

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

作者： Huang, Chengjie Abdelzad, Vandat Sedwards, Sean Czarnecki, Krzysztof Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350318920;9798350318937

We consider the problem of cross-sensor domain adaptation in the context of LidAR-based 3d object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to generate high quality pseudo-labels for stationary objects. In contrast to the current state-of-the-art in-domain practice of aggregating just a few input scans, SOAP aggregates entire sequences of point clouds at the input level to reduce the sensor domain gap. Then, by means of what we call quasi-stationary training and spatial consistency post-processing, the SOAP model generates accurate pseudo-labels for stationary objects, closing a minimum of 30.3% domain gap compared to few-frame detectors. Our results also show that state-of-the-art domain adaptation approaches can achieve even greater performance in combination with SOAP, in both the unsupervised and semi-supervised settings.

关键词： 3d computer vision Algorithms Applications Autonomous driving

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Attentive Prototypes for Source-free Unsupervised domain Adaptive 3d Object detection

Attentive Prototypes for Source-free Unsupervised Domain Ada...

引用

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

作者： Hegde, deepti Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350318920;9798350318937

3d object detection networks tend to be biased towards the data they are trained on. It has been demonstrated that the evaluation on datasets captured in different locations, conditions or with sensors of different specifications than that of the training (source) data results in a drop in model performance due to the domain gap with the test (or target) data. Current methods for adapting to the target domain data either assume access to source data during training, which may not be available due to privacy or memory concerns, or require a sequence of LidAR frames as an input. We propose a single-frame approach for source-free, unsupervised domain adaptation of LidAR-based 3d object detectors that uses class prototypes to mitigate the effect of pseudo-label noise. Addressing the limitations of traditional feature aggregation methods for prototype computation in the presence of noisy labels, we utilize a transformer module to identify outlier regions that correspond to incorrect, over-confident annotations, and compute an attentive class prototype. The losses associated with noisy pseudo-labels are down-weighed in the process of self-training. We demonstrate our approach on two recent object detectors and show that our method outperforms recent source-free domain adaptation works as well as those that leverage source information during training. The code will be made available.

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

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ARAH: Animatable Volume Rendering of Articulated Human SdFs 1

引用

17th European Conference on computer vision (ECCV)

作者： Wang, Shaofei Schwarz, Katja Geiger, Andreas Tang, Siyu Swiss Fed Inst Technol Zurich Switzerland Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany

ISBN: (数字)9783031198243

ISBN: (纸本)9783031198236;9783031198243

Combining human body models with differentiable rendering has recently enabled animatable avatars of clothed humans from sparse sets of multi-view RGB videos. While state-of-the-art approaches achieve a realistic appearance with neural radiance fields (NeRF), the inferred geometry often lacks detail due to missing geometric constraints. Further, animating avatars in out-of-distribution poses is not yet possible because the mapping from observation space to canonical space does not generalize faithfully to unseen poses. In this work, we address these shortcomings and propose a model to create animatable clothed human avatars with detailed geometry that generalize well to out-of-distribution poses. To achieve detailed geometry, we combine an articulated implicit surface representation with volume rendering. For generalization, we propose a novel joint root-finding algorithm for simultaneous ray-surface intersection search and correspondence search. Our algorithm enables efficient point sampling and accurate point canonicalization while generalizing well to unseen poses. We demonstrate that our proposed pipeline can generate clothed avatars with high-quality pose-dependent geometry and appearance from a sparse set of multi-view RGB videos. Our method achieves state-of-the-art performance on geometry and appearance reconstruction while creating animatable avatars that generalize well to out-of-distribution poses beyond the small number of training poses.

关键词： 3d computer vision Clothed human modeling Cloth modeling Neural rendering Neural implicit functions

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FocusTune: Tuning Visual Localization through Focus-Guided Sampling

FocusTune: Tuning Visual Localization through Focus-Guided S...

引用

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

作者： Nguyen, Son Tung Fontan, Alejandro Milford, Michael Fischer, Tobias Queensland Univ Technol Brisbane Qld Australia

ISBN: (纸本)9798350318920;9798350318937

We propose FocusTune, a focus-guided sampling technique to improve the performance of visual localization algorithms. FocusTune directs a scene coordinate regression model towards regions critical for 3d point triangulation by exploiting key geometric constraints. Specifically, rather than uniformly sampling points across the image for training the scene coordinate regression model, we instead re-project 3d scene coordinates onto the 2d image plane and sample within a local neighborhood of the re-projected points. While our proposed sampling strategy is generally applicable, we showcase FocusTune by integrating it with the recently introduced Accelerated Coordinate Encoding (ACE) model. Our results demonstrate that FocusTune both improves or matches state-of-the-art performance whilst keeping ACE's appealing low storage and compute requirements, for example reducing translation error from 25 to 19 and 17 to 15 cm for single and ensemble models, respectively, on the Cambridge Landmarks dataset. This combination of high performance and low compute and storage requirements is particularly promising for applications in areas like mobile robotics and augmented reality. We made our code available at https: //***/sontung/focus-tune.

关键词： 3d computer vision Algorithms

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AssemblyNet: A Point Cloud dataset and Benchmark for Predicting Part directions in an Exploded Layout

AssemblyNet: A Point Cloud Dataset and Benchmark for Predict...

引用

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

作者： Gaarsdal, Jesper Haurum, Joakim Bruslund Wolff, Sune Madsen, Claus Brondgaard SynergyXR ApS Aarhus Denmark Aalborg Univ Visual Anal & Percept VAP Lab Aalborg Denmark Pioneer Ctr AI Aalborg Denmark Aalborg Univ Comp Graph Grp Aalborg Denmark

ISBN: (纸本)9798350318920;9798350318937

Exploded views are powerful tools for visualizing the assembly and disassembly of complex objects, widely used in technical illustrations, assembly instructions, and product presentations. Previous methods for automating the creation of exploded views are either slow and computationally costly or compromise on accuracy. Therefore, the construction of exploded views is typically a manual process. In this paper, we propose a novel approach for automatically predicting the direction of parts in an exploded view using deep learning. To achieve this, we introduce a new dataset, AssemblyNet, which contains point cloud data sampled from 3d models of real-world assemblies, including water pumps, mixed industrial assemblies, and LEGO models. The AssemblyNet dataset includes a total of 44 assemblies, separated into 495 subassemblies with a total of 5420 parts. We provide ground truth labels for regression and classification, representing the directions in which the parts are moved in the exploded views. We also provide performance benchmarks using various state-of-the-art models for shape classification on point clouds and propose a novel two-path network architecture. Project page available at https://***/jgaarsdal/AssemblyNet

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

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MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video

MoRF: Mobile Realistic Fullbody Avatars from a Monocular Vid...

引用

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

作者： Bashirov, Renat Larionov, Alexey Ustinova, Evgeniya Sidorenko, Mikhail Svitov, david Zakharkin, Ilya Lempitsky, Victor Samsung Res Seoul South Korea ZERO10 Limassol Cyprus Cinemers Labs London England

ISBN: (纸本)9798350318920;9798350318937

We present a system to create Mobile Realistic Fullbody (MoRF) avatars. MoRF avatars are rendered in real-time on mobile devices, learned from monocular videos, and have high realism. We use SMPL-X as a proxy geometry and render it with dNR (neural texture and image-2-image network). We improve on prior work, by overfitting perframe warping fields in the neural texture space, allowing to better align the training signal between different frames. We also refine SMPL-X mesh fitting procedure to improve the overall avatar quality. In the comparisons to other monocular video-based avatar systems, MoRF avatars achieve higher image sharpness and temporal consistency. Participants of our user study also preferred avatars generated by MoRF.

关键词： 3d computer vision Algorithms Algorithms Algorithms Biometrics body pose Computational photography face gesture image and video synthesis

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GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo

GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent...

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

作者： Vats, Vibhas K. Joshi, Sripad Crandall, david J. Reza, Md Alimoor Jung, Soon-heung Indiana Univ Bloomington Bloomington IN 47405 USA Drake Univ Des Moines IA 50311 USA ETRI Daejeon South Korea

ISBN: (纸本)9798350318920;9798350318937

Traditional multi-view stereo (MVS) methods rely heavily on photometric and geometric consistency constraints, but newer machine learning-based MVS methods check geometric consistency across multiple source views only as a post-processing step. In this paper, we present a novel approach that explicitly encourages geometric consistency of reference view depth maps across multiple source views at different scales during learning (see Fig. 1). We find that adding this geometric consistency loss significantly accelerates learning by explicitly penalizing geometrically inconsistent pixels, reducing the training iteration requirements to nearly half that of other MVS methods. Our extensive experiments show that our approach achieves a new state-of-the-art on the dTU and BlendedMVS datasets, and competitive results on the Tanks and Temples benchmark. To the best of our knowledge, GC-MVSNet is the first attempt to enforce multi-view, multi-scale geometric consistency during learning.

关键词： 3d computer vision Algorithms

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Improving Graph Networks through Selection-based Convolution

Improving Graph Networks through Selection-based Convolution

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

作者： Hart, david Morse, Bryan East Carolina Univ Greenville NC 27858 USA Brigham Young Univ Provo UT 84602 USA

ISBN: (纸本)9798350318920;9798350318937

Graph Convolutional Networks (GCNs) provide a general framework that can learn in a variety of data domains, such as 3d geometry, social networks, and chemical structures. GCNs, however, often ignore intrinsic relationships among nodes in the graph, and these relationships need to be learned indirectly during the training process through mechanisms such as attention or local-kernel approximation. This paper introduces selection-based graph convolution, a method for preserving these intrinsic relationships within the graph convolution operator which provides improved performance over attention-based counterparts on various tasks. We demonstrate the effectiveness of selection to improve the performance of many types of GCNs on tasks such as spatial graph classification. Furthermore, we demonstrate the ability to improve state-of-the-art graph networks for road traffic estimation and molecular property prediction.

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

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