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检索条件"主题词=3D Computer Vision"

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3d Reconstruction of Interacting Multi-Person in Clothing from a Single Image

3D Reconstruction of Interacting Multi-Person in Clothing fr...

引用

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

作者： Cha, Junuk Lee, Hansol Kim, Jaewon Truong, Nhat Nguyen Bao Yoon, Jaeshin Baek, Seungryul UNIST Seoul South Korea KIST Seoul South Korea Korea Adv Inst Sci & Technol Seoul South Korea Adobe Res San Francisco CA 94107 USA

ISBN: (纸本)9798350318920;9798350318937

This paper introduces a novel pipeline to reconstruct the geometry of interacting multi-person in clothing on a globally coherent scene space from a single image. The main challenge arises from the occlusion: a part of a human body is not visible from a single view due to the occlusion by others or the self, which introduces missing geometry and physical implausibility (e.g., penetration). We overcome this challenge by utilizing two human priors for complete 3d geometry and surface contacts. For the geometry prior, an encoder learns to regress the image of a person with missing body parts to the latent vectors;a decoder decodes these vectors to produce 3d features of the associated geometry;and an implicit network combines these features with a surface normal map to reconstruct a complete and detailed 3d humans. For the contact prior, we develop an image-space contact detector that outputs a probability distribution of surface contacts between people in 3d. We use these priors to globally refine the body poses, enabling the penetration-free and accurate reconstruction of interacting multi-person in clothing on the scene space. The results demonstrate that our method is complete, globally coherent, and physically plausible compared to existing methods.

关键词： 3d 3d computer vision Algorithms Algorithms Algorithms Biometrics body pose etc. face Generative models for image gesture video

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Test-Time Adaptation for depth Completion

Test-Time Adaptation for Depth Completion

引用

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

作者： Park, Hyoungseob Gupta, Anjali Wong, Alex Yale Vision Lab New Haven CT 06501 USA

ISBN: (纸本)9798350353006

It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap, such as domain adaptation (dA), may require the source data on which the model was trained (often not available), while others, i.e., source-free dA, require many passes through the testing data. We propose an online test-time adaptation method for depth completion, the task of inferring a dense depth map from a single image and associated sparse depth map, that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image, we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. during test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%. Code available at ***/seobbro/TTA-depth-completion.

关键词： 3d computer vision depth completion depth estimation Multi-modal fusion Test time adaptation

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Unsupervised 3d Pose Estimation with Non-Rigid Structure-from-Motion Modeling

Unsupervised 3D Pose Estimation with Non-Rigid Structure-fro...

引用

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

作者： Ji, Haorui deng, Hui dai, Yuchao Li, Hongdong Australian Natl Univ Canberra ACT Australia Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Most existing 3d human pose estimation work rely heavily on the powerful memory capability of networks to obtain suitable 2d-3d mappings from the training data. Few works have studied the modeling of human posture deformation in motion. In this paper, we propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3d human skeletons in motion into the estimation of a 3d reference skeleton, and a frame-by-frame skeleton deformation. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3d reference skeleton and the skeleton deformation of each frame from 2d observations sequence, and then sum them up to obtain the pose of each frame. Subsequently, a loss term based on the diffusion model is used to ensure that the pipeline learns the correct prior motion knowledge. Finally, we have evaluated our proposed method on mainstream datasets and obtained superior results outperforming the state-of-the-art.

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

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Towards Realistic Generative 3d Face Models

Towards Realistic Generative 3D Face Models

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

作者： Rai, Aashish Gupta, Hiresh Pandey, Ayush Carrasco, Francisco Vicente Takagi, Shingo Jason Aubel, Amaury Kim, daeil Prakash, Aayush de la Torre, Fernando Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta Real Labs Menlo Pk CA USA

ISBN: (纸本)9798350318920;9798350318937

In recent years, there has been significant progress in 2d generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3d information, these 2d models often struggle to accurately disentangle facial attributes like pose, expression, and illumination, limiting their editing capabilities. To address this limitation, this paper proposes a 3d controllable generative face model to produce high-quality albedo and precise 3d shapes by leveraging existing 2d generative models. By combining 2d face generative models with semantic face manipulation, this method enables editing of detailed 3d rendered faces. The proposed framework utilizes an alternating descent optimization approach over shape and albedo. differentiable rendering is used to train high-quality shapes and albedo without 3d supervision. Moreover, this approach outperforms most state-of-the-art (SOTA) methods in the well-known NoW and REALY benchmarks for 3d face reconstruction. It also outperforms the SOTA reconstruction models in recovering rendered faces' identities across novel poses. Additionally, the paper demonstrates direct control of expressions in 3d faces by exploiting latent space leading to text-based editing of 3d faces.

关键词： 3d 3d computer vision Algorithms Algorithms Algorithms Biometrics body pose etc face Generative models for image gesture video

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U3dS³: Unsupervised 3d Semantic Scene Segmentation

U3DS<SUP>3</SUP>: Unsupervised 3D Semantic Scene Segmentatio...

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

作者： Liu, Jiaxu Yu, Zhengdi Breckon, Toby P. Shum, Hubert P. H. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (纸本)9798350318920;9798350318937

Contemporary point cloud segmentation approaches largely rely on richly annotated 3d training data. However, it is both time-consuming and challenging to obtain consistently accurate annotations for such 3d scene data. Moreover, there is still a lack of investigation into fully unsupervised scene segmentation for point clouds, especially for holistic 3d scenes. This paper presents U3dS3, as a step towards completely unsupervised point cloud segmentation for any holistic 3d scenes. To achieve this, U3dS3 leverages a generalized unsupervised segmentation method for both object and background across both indoor and outdoor static 3d point clouds with no requirement for model pre-training, by leveraging only the inherent information of the point cloud to achieve full 3d scene segmentation. The initial step of our proposed approach involves generating superpoints based on the geometric characteristics of each scene. Subsequently, it undergoes a learning process through a spatial clustering-based methodology, followed by iterative training using pseudo-labels generated in accordance with the cluster centroids. Moreover, by leveraging the invariance and equivariance of the volumetric representations, we apply the geometric transformation on voxelized features to provide two sets of descriptors for robust representation learning. Finally, our evaluation provides state-of-the-art results on the ScanNet and SemanticKITTI, and competitive results on the S3dIS, benchmark datasets.

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

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Toward Planet-Wide Traffic Camera Calibration

Toward Planet-Wide Traffic Camera Calibration

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

作者： Vuong, Khiem Tamburo, Robert Narasimhan, Srinivasa G. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350318920;9798350318937

despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges. The absence of precise camera calibration data, including intrinsic and extrinsic parameters, hinders accurate real-world distance measurements from captured videos. To address this, we present a scalable framework that utilizes street-level imagery to reconstruct a metric 3d model, facilitating precise calibration of in-the-wild traffic cameras. Notably, our framework achieves 3d scene reconstruction and accurate localization of over 100 global traffic cameras and is scalable to any camera with sufficient street-level imagery. For evaluation, we introduce a dataset of 20 fully calibrated traffic cameras, demonstrating our method's significant enhancements over existing automatic calibration techniques. Furthermore, we highlight our approach's utility in traffic analysis by extracting insights via 3d vehicle reconstruction and speed measurement, thereby opening up the potential of using outdoor cameras for automated analysis. Code and dataset will be available on the project website.

关键词： 3d computer vision Algorithms Applications Applications Autonomous driving Social good

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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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MSCC: Multi-Scale Transformers for Camera Calibration

MSCC: Multi-Scale Transformers for Camera Calibration

引用

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

作者： Song, Xu Kang, Hao Moteki, Atsunori Suzuki, Genta Kobayashi, Yoshie Tan, Zhiming Fujitsu R&D Ctr Co Ltd Beijing Peoples R China Fujitsu Ltd Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Camera calibration is very important for some vision tasks, like rendering 3d scenes, environment reconstruction, and self-localization, etc. In this paper, we propose a framework of multi-scale transformers for camera calibration. With the input of a single image, the multi-scale features output from the model's backbone are utilized to estimate camera parameters. At the same time, we show that the way of coarse-to-fine is effective to locate global structures and detailed features in the image, by studying the attention response of horizon line estimation. Moreover, deep supervision is proven to get more precise results and accelerated training. Our method outperforms all the state-of-the-art methods by objective and subjective experiments on Google Street View dataset and Pano360.

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

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HdMNet: A Hierarchical Matching Network with double Attention for Large-scale Outdoor LidAR Point Cloud Registration

HDMNet: A Hierarchical Matching Network with Double Attentio...

引用

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

作者： Xue, Weiyi Lu, Fan Chen, Guang Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Outdoor LidAR point clouds are typically large-scale and complexly distributed. To achieve efficient and accurate registration, emphasizing the similarity among local regions and prioritizing global local-to-local matching is of utmost importance, subsequent to which accuracy can be enhanced through cost-effective fine registration. In this paper, a novel hierarchical neural network with double attention named HdMNet is proposed for large-scale outdoor LidAR point cloud registration. Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to-patch manner, which significantly improves the registration performance. Moreover, in order to further utilize the sparse matching information from deeper layer, we develop a novel trainable embedding mask to incorporate the confidence scores of correspondences obtained from pose estimation of deeper layer, eliminating additional computations. The high-confidence keypoints in the sparser point cloud of the deeper layer correspond to a high-confidence spatial neighborhood region in shallower layer, which will receive more attention, while the features of non-key regions will be masked. Extensive experiments are conducted on two large-scale outdoor LidAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HdMNet.

关键词： 3d computer vision Algorithms

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MULTISCALE REPRESENTATIONS LEARNING TRANSFORMER FRAMEWORK FOR POINT CLOUd CLASSIFICATION 30

MULTISCALE REPRESENTATIONS LEARNING TRANSFORMER FRAMEWORK FO...

引用

30th IEEE International Conference on Image Processing (ICIP)

作者： Sun, Yajie Zia, Ali Zhou, Jun Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia CSIRO Agr & Food Northam WA Australia Australian Natl Univ Coll Sci Canberra ACT Australia

ISBN: (纸本)9781728198354

Extracting and aggregating multiple feature representations from various scales have become the key to point cloud classification tasks. vision Transformer (ViT) is a representative solution along this line, but it lacks the capability to model detailed multi-scale features and their interactions. In addition, learning efficient and effective representation from the point cloud is challenging due to its irregular, unordered, and sparse nature. To tackle these problems, we propose a novel multi-scale representation learning transformer framework, employing various geometric features beyond common Cartesian coordinates. Our approach enriches the description of point clouds by local geometric relationships and group them at multiple scales. This scale information is aggregated and then new patches can be extracted to minimize feature overlay. The bottleneck projection head is then adopted to enhance the information and feed all patches to the multi-head attention to capture the deep dependencies among representations across patches. Evaluation on public benchmark datasets shows the competitive performance of our framework on point cloud classification.

关键词： Point cloud classification multi-scale features geometric features multi-scale transformer 3d computer vision

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