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Learning to Reconstruct Texture-less Deformable Surfaces from a Single View [article]

Jan Bednařík, Pascal Fua, Mathieu Salzmann
2018 arXiv   pre-print
Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured.  ...  By contrast, recovering the 3D shape of texture-less surfaces remains an open problem, and essentially relates to Shape-from-Shading.  ...  Acknowledgments This work was supported in part by a Swiss National Foundation for Research grant.  ... 
arXiv:1803.08908v2 fatcat:2mszsqtnobbyfkjmodvnksrxha

Learning to Reconstruct Texture-Less Deformable Surfaces from a Single View

Jan Bednarik, Pascal Fua, Mathieu Salzmann
2018 2018 International Conference on 3D Vision (3DV)  
Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured.  ...  Our experiments show that meshes are ill-suited to handle texture-less 3D reconstruction in our context.  ...  Acknowledgments This work was supported in part by a Swiss National Foundation for Research grant.  ... 
doi:10.1109/3dv.2018.00075 dblp:conf/3dim/BednarikFS18 fatcat:xdmccjdnofh37jy34s6nunyrcu

Deep Textured 3D Reconstruction of Human Bodies [article]

Abbhinav Venkat, Sai Sagar Jinka, Avinash Sharma
2018 arXiv   pre-print
In this paper, we propose a deep learning based solution for textured 3D reconstruction of human body shapes from a single view RGB image.  ...  Further, a calibration-free environment adds additional complexity to both - reconstruction and texture recovery.  ...  In order to ensure that reconstruction is feasible from single as well as multiple views, we choose random number of views from available views for training a mesh in each iteration.  ... 
arXiv:1809.06547v1 fatcat:cysa6kojvrd6zeqvxcmwf3ntva

Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks

Mingyun Wen, Jisun Park, Kyungeun Cho
2021 Remote Sensing  
This study focuses on reconstructing accurate meshes with high-resolution textures from single images.  ...  The mesh-reconstruction network estimates a deformation map, which is used to deform a template mesh to the shape of the target object in the input image, and a low-resolution texture.  ...  Conclusions In this study, we proposed a method for reconstructing a high-resolution textured mesh from a single image.  ... 
doi:10.3390/rs13214254 fatcat:m7nwlbqp55gqbmcodx7ap4rhhe

Self-supervised Single-view 3D Reconstruction via Semantic Consistency [article]

Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Varun Jampani, Ming-Hsuan Yang, Jan Kautz
2020 arXiv   pre-print
We learn a self-supervised, single-view 3D reconstruction model that predicts the 3D mesh shape, texture and camera pose of a target object with a collection of 2D images and silhouettes.  ...  To the best of our knowledge, we are the first to try and solve the single-view reconstruction problem without a category-specific template mesh or semantic keypoints.  ...  Inspired by this intuition, we learn a single-view reconstruction model from a collection of images and silhouettes.  ... 
arXiv:2003.06473v1 fatcat:kf4djdg7d5ddzb2wibdlzxfltu

Topologically-Aware Deformation Fields for Single-View 3D Reconstruction [article]

Shivam Duggal, Deepak Pathak
2022 arXiv   pre-print
At inference time, given a single image, we reconstruct the underlying 3D shape by first implicitly deforming each 3D point in the object space to the learned category-specific canonical space using the  ...  The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned image collections and their poses without  ...  The proposed approach, TARS, tackles the problem of single-view reconstruction by implicitly learning to deform different object instances to a learned category specific mean shape.  ... 
arXiv:2205.06267v2 fatcat:4tnngz3oirh7boxubsmv42c43i

Learning Category-Specific Mesh Reconstruction from Image Collections [chapter]

Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, Jitendra Malik
2018 Lecture Notes in Computer Science  
We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image.  ...  Texture Camera Shape f Fig. 1 : Given an annotated image collection of an object category, we learn a predictor f that can map a novel image I to its 3D shape, camera pose, and texture.  ...  Single-view 3D Reconstruction. We show sample reconstruction results on images from the CUB test set in Figure 5 .  ... 
doi:10.1007/978-3-030-01267-0_23 fatcat:p3yaq7ctdbh4zfrpwjtlo23caq

Online Adaptation for Consistent Mesh Reconstruction in the Wild [article]

Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz
2020 arXiv   pre-print
We first learn a category-specific 3D reconstruction model from a collection of single-view images of the same category that jointly predicts the shape, texture, and camera pose of an image.  ...  This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild.  ...  We learn a category-specific 3D mesh reconstruction model that jointly predicts the shape, texture, and camera pose from single-view images, which is capable of capturing asymmetric non-rigid motion deformation  ... 
arXiv:2012.03196v1 fatcat:vpvkmtu3qvav7gpgs4ty5ryugq

PeeledHuman: Robust Shape Representation for Textured 3D Human Body Reconstruction [article]

Sai Sagar Jinka, Rohan Chacko, Avinash Sharma, P. J. Narayanan
2020 arXiv   pre-print
In our simple non-parametric solution, the generated Peeled Depth maps are back-projected to 3D space to obtain a complete textured 3D shape.  ...  Given a monocular RGB image, we learn these Peeled maps in an end-to-end generative adversarial fashion using our novel framework - PeelGAN.  ...  Conclusion We present a novel representation to reconstruct a textured human model from a single RGB image using Peeled Depth and RGB maps.  ... 
arXiv:2002.06664v2 fatcat:itfkq7qf6vc3jhoa4qmgnatnr4

Learning Category-Specific Mesh Reconstruction from Image Collections [article]

Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, Jitendra Malik
2018 arXiv   pre-print
We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image.  ...  The shape is represented as a deformable 3D mesh model of an object category where a shape is parameterized by a learned mean shape and per-instance predicted deformation.  ...  Single-view 3D Reconstruction. We show sample reconstruction results on images from the CUB test set in Figure 5 .  ... 
arXiv:1803.07549v2 fatcat:ivunso4vqfav5pi65ftqjmsh2e

When 3D Reconstruction Meets Ubiquitous RGB-D Images

Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
3D reconstruction from a single image is a classical problem in computer vision. However, it still poses great challenges for the reconstruction of daily-use objects with irregular 1 shapes.  ...  The learning of 3D reconstruction is defined as a category modeling problem, in which a model for each category is trained to encode category-specific knowledge for 3D reconstruction.  ...  [9] proposed to learn 3D primitives from RGB-D images for single-view reconstruction of indoor environment.  ... 
doi:10.1109/cvpr.2014.95 dblp:conf/cvpr/ZhangSSZS14 fatcat:7a4ofyaqufeehbubjmgw33ezjm

Local deformation models for monocular 3D shape recovery

Mathieu Salzmann, Raquel Urtasun, Pascal Fua
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
While using a texture-based approach, we show that our models are effective to reconstruct from single videos poorly-textured surfaces of arbitrary shape, made of materials as different as cardboard, that  ...  By contrast with typical statistical learning methods that build models for specific object shapes, we learn local deformation models, and combine them to reconstruct surfaces of arbitrary global shapes  ...  Introduction Without a deformation model, recovering the 3D shape of a non-rigid surface from a single view is an ill-posed problem.  ... 
doi:10.1109/cvpr.2008.4587499 dblp:conf/cvpr/SalzmannUF08 fatcat:rzjgyy2ug5di3jwkggvuddek74

Monocular 3D Reconstruction of Locally Textured Surfaces

A. Varol, A. Shaji, M. Salzmann, P. Fua
2012 IEEE Transactions on Pattern Analysis and Machine Intelligence  
At the heart of our algorithm are a learned mapping from intensity patterns to the shape of local surface patches and a principled approach to piecing together the resulting local shape estimates.  ...  Here, we propose a novel approach to monocular deformable shape recovery that can operate under complex lighting and handle partially textured surfaces.  ...  INTRODUCTION Many algorithms have been proposed to recover the 3D shape of a deformable surface from either single views or short video sequences.  ... 
doi:10.1109/tpami.2011.196 pmid:22516648 fatcat:gywsvcv2trej5k6ezdsb5x4ugm

Detailed Avatar Recovery from Single Image [article]

Hao Zhu and Xinxin Zuo and Haotian Yang and Sen Wang and Xun Cao and Ruigang Yang
2021 arXiv   pre-print
This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints.  ...  We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information.  ...  TEXTURE COMPLETION Synthesizing complete texture for the reconstructed human model from a single image is also a challenging problem, since only less than half of the texture is visible and can be retrieved  ... 
arXiv:2108.02931v1 fatcat:66rbl5dhnvdsvbatfoo5znaabm

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization [article]

Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, Hao Li
2019 arXiv   pre-print
Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images  ...  Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views.  ...  The multi-view PIFu is fine-tuned from the models trained for single-view surface reconstruction and texture inference with a learning rate of 1 × 10 −4 and 2 epochs.  ... 
arXiv:1905.05172v3 fatcat:aq7mo4wt6nea5cpka24azjswba
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