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检索条件"主题词=3D Computer Vision"

共 243 条记录，以下是91-100 订阅

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MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction

MPT: Mesh Pre-Training with Transformers for Human Pose and ...

引用

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

作者： Lin, Kevin Lin, Chung-Ching Liang, Lin Liu, Zicheng Wang, Lijuan Microsoft Redmond WA 98052 USA

ISBN: (纸本)9798350318920;9798350318937

Traditional methods of reconstructing 3d human pose and mesh from single images rely on paired image-mesh datasets, which can be difficult and expensive to obtain. due to this limitation, model scalability is constrained as well as reconstruction performance. Towards addressing the challenge, we introduce Mesh Pre-Training (MPT), an effective pre-training strategy that leverages large amounts of MoCap data to effectively perform pre-training at scale. We introduce the use of MoCap-generated heatmaps as input representations to the mesh regression transformer and propose a Masked Heatmap Modeling approach for improving pre-training performance. This study demonstrates that pre-training using the proposed MPT allows our models to perform effective inference without requiring fine-tuning. We further show that fine-tuning the pre-trained MPT model considerably improves the accuracy of human mesh reconstruction from single images. Experimental results show that MPT outperforms previous state-of-the-art methods on Human3.6M and 3dPW datasets. As a further application, we benchmark and study MPT on the task of 3d hand reconstruction, showing that our generic pre-training scheme generalizes well to hand pose estimation and achieves promising reconstruction performance.

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

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Sharp-NeRF: Grid-based Fast deblurring Neural Radiance Fields using Sharpness Prior

Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Field...

引用

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

作者： Lee, Byeonghyeon Lee, Howoong Ali, Usman Park, Eunbyung Sungkyunkwan Univ Dept Artificial Intelligence Seoul South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Seoul South Korea Hanwha Vis Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Neural Radiance Fields (NeRF) has shown its remarkable performance in neural rendering-based novel view synthesis. However, NeRF suffers from severe visual quality degradation when the input images have been captured under imperfect conditions, such as poor illumination, defocus blurring and lens aberrations. Especially, defocus blur is quite common in the images when they are normally captured using cameras. Although few recent studies have proposed to render sharp images of considerably high-quality, yet they still face many key challenges. In particular, those methods have employed a Multi-Layer Perceptron (MLP) based NeRF which requires tremendous computational time. To overcome these shortcomings, this paper proposes a novel technique Sharp-NeRF-a grid-based NeRF that renders clean and sharp images from the input blurry images within a half an hour training. To do so, we used several grid-based kernels to accurately model the sharpness/blurriness of the scene. The sharpness level of the pixels is computed to learn the spatially varying blur kernels. We have conducted experiments on the benchmarks consisting of blurry images and have evaluated full-reference and non-reference metrics. The qualitative and quantitative results have revealed that our approach renders the sharp novel views with vivid colors and fine details, and it has considerably faster training time than the previous works. Our code is available at https://***/benhenryL/SharpNeRF.

关键词： 3d computer vision Algorithms Algorithms Image recognition and understanding

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Panelformer: Sewing Pattern Reconstruction from 2d Garment Images

Panelformer: Sewing Pattern Reconstruction from 2D Garment I...

引用

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

作者： Chen, Cheng-Hsiu Su, Jheng-Wei Hu, Min-Chun Yao, Chih-Yuan Chu, Hung-Kuo Natl Tsing Hua Univ Hsinchu Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (纸本)9798350318920;9798350318937

In this paper, we present a novel approach for reconstructing garment sewing patterns from 2d garment images. Our method addresses the challenge of handling occlusion in 2d images by leveraging the symmetric and correlated nature of garment panels. We introduce a transformer-based deep neural network called Panelformer that learns the parametric space of garment sewing patterns. The network comprises two components: the panel transformer and the stitch predictor. The panel transformer estimates the parametric panel shapes, including the occluded panels, by learning from the visible ones. The stitch predictor determines the stitching information among the predicted panels, enabling the reconstruction of the complete garment. To mitigate the overfitting problem caused by strong panel correlations, we propose two tailor-made data augmentation techniques: panel masking and garment mixing. These techniques generate a wider variety of panel combinations, enhancing the model's robustness and generalization capability. We evaluate the effectiveness of Panelformer using a synthetic dataset with diverse garment types. The experimental results demonstrate that our method outperforms competing baselines and achieves comparable performance to NeuralTailor, which operates on 3d point cloud data. This validates the efficacy of our approach in the context of garment sewing pattern reconstruction. By utilizing 2d images as input, our method expands the potential applications of garment modeling and offers easy accessibility to end users. Our code is available online(1).

关键词： 3d computer vision Algorithms Algorithms Image recognition and understanding

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Fusing directions and displacements in Translation Averaging 11

Fusing Directions and Displacements in Translation Averaging

引用

International Conference in 3d vision (3dV)

作者： Manam, Lalit Govindu, Venu Madhav Indian Inst Sci Bengaluru 560012 India

ISBN: (纸本)9798350362466;9798350362459

Translation averaging solves for 3d camera translations given many pairwise relative translation directions. The mismatch between inputs (directions) and output estimates (absolute translations) makes translation averaging a challenging problem, which is often addressed by comparing either directions or displacements using relaxed cost functions that are relatively easy to optimize. However, the distinctly different nature of the cost functions leads to varied behaviour under different baselines and noise conditions. In this paper, we argue that translation averaging can benefit from a fusion of the two approaches. Specifically, we recursively fuse the individual updates suggested by direction and displacement-based methods using their uncertainties. The uncertainty of each estimate is modelled by the inverse of the Hessian of the corresponding optimization problem. As a result, our method utilizes the advantages of both methods in a principled manner. The superiority of our translation averaging scheme is demonstrated via the improved accuracies of camera translations on benchmark datasets compared to the state-of-the-art methods.

关键词： 3d computer vision Structure-from-Motion Translation Averaging

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OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision

OptFlow: Fast Optimization-based Scene Flow Estimation witho...

引用

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

作者： Ahuja, Rahul Baker, Chris Schwarting, Wilko ISEE AI Cambridge MA 02139 USA

ISBN: (纸本)9798350318920;9798350318937

Scene flow estimation is a crucial component in the development of autonomous driving and 3d robotics, providing valuable information for environment perception and navigation. despite the advantages of learning-based scene flow estimation techniques, their domain specificity and limited generalizability across varied scenarios pose challenges. In contrast, non-learning optimization-based methods, incorporating robust priors or regularization, offer competitive scene flow estimation performance, require no training, and show extensive applicability across datasets, but suffer from lengthy inference times. In this paper, we present OptFlow, a fast optimization-based scene flow estimation method. Without relying on learning or any labeled datasets, OptFlow achieves state-of-the-art performance for scene flow estimation on popular autonomous driving benchmarks. It integrates a local correlation weight matrix for correspondence matching, an adaptive correspondence threshold limit for nearest-neighbor search, and graph prior rigidity constraints, resulting in expedited convergence and improved point correspondence identification. Moreover, we demonstrate how integrating a point cloud registration function within our objective function bolsters accuracy and differentiates between static and dynamic points without relying on external odometry data. Consequently, OptFlow outperforms the baseline graph-prior method by approximately 20% and the Neural Scene Flow Prior method by 5%-7% in accuracy, all while offering the fastest inference time among all non-learning scene flow estimation methods.

关键词： 3d computer vision Algorithms Applications Applications Autonomous driving Robotics

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UnScene3d: Unsupervised 3d Instance Segmentation for Indoor Scenes

UnScene3D: Unsupervised 3D Instance Segmentation for Indoor ...

引用

IEEE/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Rozenberszki, david Litany, Or dai, Angela Tech Univ Munich Munich Germany Technion Haifa Israel NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350353006

3d instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3d scenes rely on supervision from expensive, manual 3d annotations. We propose UnScene3d, the first fully unsupervised 3d learning approach for class-agnostic 3d instance segmentation of indoor scans. UnScene3d first generates pseudo masks by leveraging self-supervised color and geometry features to find potential object regions. We operate on a basis of geometric oversegmentation, enabling efficient representation and learning on high-resolution 3d data. The coarse proposals are then refined through self-training our model on its predictions. Our approach improves over clustering-based alternatives to unsupervised 3d instance segmentation methods by more than 300% Average Precision score, demonstrating effective instance segmentation even in challenging, cluttered 3d scenes.

关键词： 3d computer vision 3d Instance Segmentation Graph-cuts Scene Understanding Self-training Unsupervised Instance Segmentation Unsupervised Learning

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LInKs "Lifting Independent Keypoints" - Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2d-3d Human Pose Estimation

LInKs "Lifting Independent Keypoints" - Partial Pose Lifting...

引用

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

作者： Hardy, Peter Kim, Hansung Univ Southampton ECS Vis Learning & Control Southampton Hants England

ISBN: (纸本)9798350318920;9798350318937

We present LInKs, a novel unsupervised learning method to recover 3d human poses from 2d kinematic skeletons obtained from a single image, even when occlusions are present. Our approach follows a unique two-step process, which involves first lifting the occluded 2d pose to the 3d domain, followed by filling in the occluded parts using the partially reconstructed 3d coordinates. This lift-then-fill approach leads to significantly more accurate results compared to models that complete the pose in 2d space alone. Additionally, we improve the stability and likelihood estimation of normalising flows through a custom sampling function replacing PCA dimensionality reduction used in prior work. Furthermore, we are the first to investigate if different parts of the 2d kinematic skeleton can be lifted independently which we find by itself reduces the error of current lifting approaches. We attribute this to the reduction of long-range keypoint correlations. In our detailed evaluation, we quantify the error under various realistic occlusion scenarios, showcasing the versatility and applicability of our model. Our results consistently demonstrate the superiority of handling all types of occlusions in 3d space when compared to others that complete the pose in 2d space. Our approach also exhibits consistent accuracy in scenarios without occlusion, as evidenced by a 7.9% reduction in reconstruction error compared to prior works on the Human3.6M dataset. Furthermore, our method excels in accurately retrieving complete 3d poses even in the presence of occlusions, making it highly applicable in situations where complete 2d pose information is unavailable.

关键词： 3d computer vision Algorithms Algorithms Algorithms and algorithms Biometrics body pose face formulations gesture Machine learning architectures

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Synergizing Contrastive Learning and Optimal Transport for 3d Point Cloud domain Adaptation

Synergizing Contrastive Learning and Optimal Transport for 3...

引用

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

作者： Katageri, Siddharth de, Arkadipta devaguptapu, Chaitanya Prasad, V. S. S. V. Sharma, Charu Kaul, Manohar IIIT Hyderabad Hyderabad India Fujitsu Res India Bengaluru India

ISBN: (纸本)9798350318920;9798350318937

Recently, the fundamental problem of unsupervised domain adaptation (UdA) on 3d point clouds has been motivated by a wide variety of applications in robotics, virtual reality, and scene understanding, to name a few. The point cloud data acquisition procedures manifest themselves as significant domain discrepancies and geometric variations among both similar and dissimilar classes. The standard domain adaptation methods developed for images do not directly translate to point cloud data because of their complex geometric nature. To address this challenge, we leverage the idea of multimodality and alignment between distributions. We propose a new UdA architecture for point cloud classification that benefits from multimodal contrastive learning to get better class separation in both domains individually. Further, the use of optimal transport (OT) aims at learning source and target data distributions jointly to reduce the cross-domain shift and provide a better alignment. We conduct a comprehensive empirical study on PointdA-10 and GraspNetPC-10 and show that our method achieves state-of-the-art performance on GraspNetPC-10 (with similar to 4-12% margin) and best average performance on PointdA-10. Our ablation studies and decision boundary analysis also validate the significance of our contrastive learning module and OT alignment. https://***/COT.

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

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Autonomous river navigation

Autonomous river navigation

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Conference on Mobile Robots XVII

作者： Snyder, Fd Morris, dd Haley, PH Collins, RT Okerholm, AM Northrop Grumman Pittsburgh PA 15221 USA

ISBN: (纸本)0819455628

Existing maritime navigation and reconnaissance systems require man-in-the-loop situation awareness for obstacle avoidance, area survey analysis, threat assessment, and mission re-planning. We have developed a boat with fully autonomous navigation, surveillance, and reactive behaviors. Autonomous water navigation is achieved with no prior maps or other data - the water surface, riverbanks obstacles, movers and salient objects are discovered and mapped in real-time using a circular array of cameras along with a self-directed pan-tilt camera. The autonomous boat has been tested on harbor and river domains. Results of the detection, tracking, mapping and navigation will be presented.

关键词： 3d computer vision autonomous navigation detection tracking multi-camera stereo

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NeRFEditor: differentiable Style decomposition for 3d Scene Editing

NeRFEditor: Differentiable Style Decomposition for 3D Scene ...

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

作者： Sun, Chunyi Liu, Yanbin Han, Junlin Gould, Stephen Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9798350318920;9798350318937

We present NeRFEditor, an efficient learning framework for 3d scene editing, which takes a video as input and outputs a high-quality, identity-preserving stylized 3d scene. Our goal is to bridge the gap between 2d and 3d editing, catering to a wide array of creative modifications such as reference-guided alterations, text-based prompts, and user interactions. We achieve this by encouraging a pre-trained StyleGAN model and a NeRF model to learn mutually consistent renderings. Specifically, we use NeRF to generate numerous (image, camera pose)-pairs to train an adjustor module, which adapts the StyleGAN latent code for generating high-fidelity stylized images from any given viewing angle. To extrapolate edits to novel views, i.e., those not seen by StyleGAN pre-training, while maintaining 360 degrees consistency, we propose a second self-supervised module that maps these views into the hidden space of StyleGAN. Together these two modules produce sufficient guidance for NeRF to learn consistent stylization effects across the full range of views. Experiments show that NeRFEditor outperforms prior work on benchmark and real-world scenes with better editability, fidelity, and identity preservation.

关键词： 3d computer vision Algorithms Algorithms Applications Arts / games / social media Computational photography image and video synthesis

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