On‐Site Example‐Based Material Appearance Acquisition

Lin, Yiming; Peers, Pieter; Ghosh, Abhijeet

doi:10.1111/cgf.13766

Cited by 11 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a long-standing problem in the computer graphics field and different strategies have been proposed, for instance, using PatchMatch [39], texture transport [40], point processes [41], [42], or neural networks [43], [44], [45], [46]. Also related to our work, Li et al [47] capture the appearance of materials by first estimating their BRDF and, then, synthesizing the Fig. 2: Overview of the method.…”

Section: Related Workmentioning

confidence: 99%

Neural Photometry-guided Visual Attribute Transfer

Rodriguez-Pardo,

Garces

2021

Preprint

View full text Add to dashboard Cite

We present a deep learning-based method for propagating spatially-varying visual material attributes (e.g. texture maps or image stylizations) to larger samples of the same or similar materials. For training, we leverage images of the material taken under multiple illuminations and a dedicated data augmentation policy, making the transfer robust to novel illumination conditions and affine deformations. Our model relies on a supervised image-to-image translation framework and is agnostic to the transferred domain; we showcase a semantic segmentation, a normal map, and a stylization. Following an image analogies approach, the method only requires the training data to contain the same visual structures as the input guidance. Our approach works at interactive rates, making it suitable for material edit applications. We thoroughly evaluate our learning methodology in a controlled setup providing quantitative measures of performance. Last, we demonstrate that training the model on a single material is enough to generalize to materials of the same type without the need for massive datasets.

show abstract

Section: Related Workmentioning

confidence: 99%

Neural Photometry-guided Visual Attribute Transfer

Rodriguez-Pardo,

Garces

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Lin et al . [LPG19] estimate appearance parameters by simply capturing HDR images of an object and a light probe. Dong et al .…”

Section: Related Workmentioning

confidence: 99%

Progressive Acquisition of SVBRDF and Shape in Motion

Baek

Nam

et al. 2020

Computer Graphics Forum

View full text Add to dashboard Cite

To estimate appearance parameters, traditional SVBRDF acquisition methods require multiple input images to be captured with various angles of light and camera, followed by a post‐processing step. For this reason, subjects have been limited to static scenes, or a multiview system is required to capture dynamic objects. In this paper, we propose a simultaneous acquisition method of SVBRDF and shape allowing us to capture the material appearance of deformable objects in motion using a single RGBD camera. To do so, we progressively integrate photometric samples of surfaces in motion in a volumetric data structure with a deformation graph. Then, building upon recent advances of fusion‐based methods, we estimate SVBRDF parameters in motion. We make use of a conventional RGBD camera that consists of the colour and infrared cameras with active infrared illumination. The colour camera is used for capturing diffuse properties, and the infrared camera‐illumination module is employed for estimating specular properties by means of active illumination. Our joint optimization yields complete material appearance parameters. We demonstrate the effectiveness of our method with extensive evaluation on both synthetic and real data that include various deformable objects of specular and diffuse appearance.

show abstract

“…Some approaches reconstruct complex material with simplified geometry. Lin et al [21] present a shape-agnostic method for on-site BRDF capture, and Gao et al [5] use data-driven methods to reconstruct SVBRDFs under planar assumptions. Other methods implicitly perform reconstruction via view synthesis.…”

Section: Geometry and Materials Reconstructionmentioning

confidence: 99%

Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing

Goel¹,

Cohen²,

Guesman³

et al. 2020

Preprint

View full text Add to dashboard Cite

Reconstructing object geometry and material from multiple views typically requires optimization. Differentiable path tracing is an appealing framework as it can reproduce complex appearance effects. However, it is difficult to use due to high computational cost. In this paper, we explore how to use differentiable ray tracing to refine an initial coarse mesh and per-mesh-facet material representation. In simulation, we find that it is possible to reconstruct fine geometric and material detail from low resolution input views, allowing high-quality reconstructions in a few hours despite the expense of path tracing. The reconstructions successfully disambiguate shading, shadow, and global illumination effects such as diffuse interreflection from material properties. We demonstrate the impact of different geometry initializations, including space carving, multi-view stereo, and 3D neural networks. Finally, with input captured using smartphone video and a consumer 360 • camera for lighting estimation, we also show how to refine initial reconstructions of real-world objects in unconstrained environments.

show abstract

On‐Site Example‐Based Material Appearance Acquisition

Cited by 11 publications

References 35 publications

Neural Photometry-guided Visual Attribute Transfer

Neural Photometry-guided Visual Attribute Transfer

Progressive Acquisition of SVBRDF and Shape in Motion

Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing

Contact Info

Product

Resources

About