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Invertible Neural BRDF for Object Inverse Rendering [article]

Zhe Chen, Shohei Nobuhara, Ko Nishino
2020 arXiv   pre-print
We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and natural illumination from a single image of an object  ...  We also devise a deep illumination prior by leveraging the structural bias of deep neural networks.  ...  Lombardi and Nishino [23] use a learned prior for natural materials using the DSBRDF model [31] for multi-material estimation. Kang et al . [17] and Gao et al .  ... 
arXiv:2008.04030v2 fatcat:e3ubqao2gvgana6si6emn4eb24

Deep Reflectance Maps [article]

Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Efstratios Gavves, Tinne Tuytelaars
2015 arXiv   pre-print
In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images.  ...  We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions.  ...  Our results show truthful reflectance maps in all three investigated scenarios and we demonstrate the applicability on several image-based editing tasks.  ... 
arXiv:1511.04384v1 fatcat:277f7lbasfafzn6k34yha3hvii

LIME: Live Intrinsic Material Estimation [article]

Abhimitra Meka, Maxim Maximov, Michael Zollhoefer, Avishek Chatterjee, Hans-Peter Seidel, Christian Richardt, Christian Theobalt
2018 arXiv   pre-print
We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input.  ...  The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo.  ...  Gool, and T. Tuytelaars. Reflectance and natural illumination from single-material specular objects using deep learning.  ... 
arXiv:1801.01075v2 fatcat:bxvrwkxkgnhq5owufhqwzgrfre

LIME: Live Intrinsic Material Estimation

Abhimitra Meka, Maxim Maximov, Michael Zollhofer, Avishek Chatterjee, Hans-Peter Seidel, Christian Richardt, Christian Theobalt
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Our approach enables the real-time estimation of the material of general objects (left) from just a single monocular color image.  ...  The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo.  ...  This work was supported by EPSRC grant CAMERA (EP/M023281/1), ERC Starting Grant CapReal (335545), and the Max Planck Center for Visual Computing and Communications (MPC-VCC).  ... 
doi:10.1109/cvpr.2018.00661 dblp:conf/cvpr/MekaMZCSRT18 fatcat:q5hkphulibfgxotmez5ifls2ym

Deep Reflectance Maps

Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Efstratios Gavves, Tinne Tuytelaars
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images.  ...  We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions.  ...  Acknowledgements This work is supported by the IWT SBO project PARIS and the FWO project "Representations and algorithms for the captation, visualization and manipulation of moving 3D objects, subjects  ... 
doi:10.1109/cvpr.2016.488 dblp:conf/cvpr/RematasRFGT16 fatcat:5thra7wcyjh7zlwiuzpjpyvac4

What Is Around The Camera? [article]

Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars, Luc Van Gool
2017 arXiv   pre-print
The proposed method allows us to jointly model the statistics of environments and material properties.  ...  We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals.  ...  To this end, we took photographs of naturally illuminated singlematerial and multi-material objects with known geometry.  ... 
arXiv:1611.09325v2 fatcat:z4i4kgth4bce3exr4hd3zjm4qe

A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond [article]

Elena Garces, Carlos Rodriguez-Pardo, Dan Casas, Jorge Lopez-Moreno
2021 arXiv   pre-print
Deep learning techniques have been broadly applied in recent years to increase the accuracy of those separations.  ...  Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and  ...  While none of the deep learning-based methods use it, the dichromatic reflection model (Section 2.3.2) was also used to account for metallic objects and colored speculars in traditional approaches [130  ... 
arXiv:2112.03842v1 fatcat:ciwwxoodq5fl7ma4jqjrgp7k5m

Robust Point Light Source Estimation Using Differentiable Rendering [article]

Grégoire Nieto, Salma Jiddi, Philippe Robert
2018 arXiv   pre-print
Illumination estimation is often used in mixed reality to re-render a scene from another point of view, to change the color/texture of an object, or to insert a virtual object consistently lit into a real  ...  We compare our differentiable renderer to state-of-the-art methods and demonstrate its robustness to an incorrect reflectance estimation.  ...  [27] learn to estimate an intermediate representation that mixes illumination and a single material, called a "reflectance map".  ... 
arXiv:1812.04857v1 fatcat:aizt5l2tmjce3od3crtgeklr4u

DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination [article]

Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Luc Van Gool, Tinne Tuytelaars
2016 arXiv   pre-print
In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i.e. from a single 2D image of a sphere of one material under one illumination.  ...  With the recent advances in estimating reflectance maps from 2D images their further decomposition has become increasingly relevant.  ...  Conclusion We have shown how Convolutional Neural Networks (CNNs) can be used to decompose a 2D image of a reflectance map into specular reflectance (material) and complex natural illumination.  ... 
arXiv:1603.08240v1 fatcat:hubbd5tauba4zgbrbbf3d4osom

Deep Appearance Maps [article]

Maxim Maximov, Laura Leal-Taixé, Mario Fritz, Tobias Ritschel
2019 arXiv   pre-print
We propose a deep representation of appearance, i. e., the relation of color, surface orientation, viewer position, material and illumination.  ...  Finally, we show the example of an appearance estimation-and-segmentation task, mapping from an image showingmultiple materials to multiple deep appearance maps.  ...  Using a single material, the material segmentation is ignored and one random material from the objects is assigned to the entire 3D objects. In the multi-material case, we proceed directly.  ... 
arXiv:1804.00863v3 fatcat:2hykyu5rzjaazjbxknkuoqzosu

Faces as Lighting Probes via Unsupervised Deep Highlight Extraction [chapter]

Renjiao Yi, Chenyang Zhu, Ping Tan, Stephen Lin
2018 Lecture Notes in Computer Science  
We present a method for estimating detailed scene illumination using human faces in a single image.  ...  Based on the observation that faces can exhibit strong highlight reflections from a broad range of lighting directions, we propose a deep neural network for extracting highlights from faces, and then trace  ...  Renjiao Yi is supported by scholarship from China Scholarship Council.  ... 
doi:10.1007/978-3-030-01240-3_20 fatcat:ljaygsbsx5ggxcgvhlbfa5p2ze

Learning Non-Lambertian Object Intrinsics Across ShapeNet Categories

Jian Shi, Yue Dong, Hao Su, Stella X. Yu
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We consider the non-Lambertian object intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object.  ...  Rendered with realistic environment maps, millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN.  ...  Our model predicts spatially varying albedo maps and supports general lighting conditions. Learning from Rendered Images. Images rendered from 3D models are widely used in deep learning, e.g.  ... 
doi:10.1109/cvpr.2017.619 dblp:conf/cvpr/ShiDSY17 fatcat:v7jfasg62zdancavcwfd35ypqe

A Method for Estimating Reflectance map and Material using Deep Learning with Synthetic Dataset [article]

Mingi Lim, Sung-eui Yoon
2020 arXiv   pre-print
In this paper, we propose a deep learning-based reflectance map prediction system for material estimation of target objects in the image, so as to alleviate the ill-posed problem that occurs in this image  ...  We get out of the previously proposed Deep Learning-based network architecture for reflectance map, and we newly propose to use conditional Generative Adversarial Network (cGAN) structures for estimating  ...  In this work, we extract high quality reflectance maps from images of a target object with complex shapes and specular materials under complex natural illumination.  ... 
arXiv:2001.05372v1 fatcat:yaqcgbk5ezbpflf56foowkhnxq

Object-based Illumination Estimation with Rendering-aware Neural Networks [article]

Xin Wei, Guojun Chen, Yue Dong, Stephen Lin, Xin Tong
2020 arXiv   pre-print
This results in a rendering-aware system that estimates the local illumination distribution at an object with high accuracy and in real time.  ...  We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas.  ...  Deep learning has also been applied for illumination estimation from objects.  ... 
arXiv:2008.02514v1 fatcat:3er3wp6ldrcotiqh26damvv7sq

Material Editing Using a Physically Based Rendering Network [article]

Guilin Liu, Duygu Ceylan, Ersin Yumer, Jimei Yang, Jyh-Ming Lien
2017 arXiv   pre-print
Specifically, given a single image, the network first predicts intrinsic properties, i.e. shape, illumination, and material, which are then provided to a rendering layer.  ...  The proposed rendering layer is fully differentiable, supports both diffuse and specular materials, and thus can be applicable in a variety of problem settings.  ...  This work is supported by a gift from Adobe, NSF EFRI-1240459, CNS-1205260 and AFOSR FA9550-17-1-0075.  ... 
arXiv:1708.00106v2 fatcat:g4z7u5uezjdrfom3t4krzeq7ra
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