Intrinsic Images by Clustering

Garcés, Elena; Muñoz, Arsenio; López‐Moreno, Jorge; Gutiérrez, Diego

doi:10.1111/j.1467-8659.2012.03137.x

Cited by 126 publications

(102 citation statements)

References 26 publications

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…Nonetheless, significant progress has been made on this problem recently, and usable decompositions of real-world scenes have been obtained by incorporating additional data such as depth maps [Lee et al 2012;Barron and Malik 2013;Chen and Koltun 2013], multiple images of the same scene [Weiss 2001;Laffont et al 2012], or sophisticated priors such as reflectance sparsity [Gehler et al 2011] and non-local texture cues [Shen et al 2008;Garces et al 2012;Laffont et al 2012;Zhao et al 2012]. Incorporating these additional terms typically result in sophisticated algorithms which take minutes to solve for a single image.…”

Section: Introductionmentioning

confidence: 99%

Interactive intrinsic video editing

et al. 2014

View full text Add to dashboard Cite

. Now, editing the textures in the reflectance image does not affect the illumination (c): changes to the brick walls, the roof tiles, and the pathway leading up to the building all maintain the complex illumination of the light through the trees. We encourage readers to zoom into this figure to see the details in our results, and refer them to the accompanying video to see the temporally consistent nature of our decomposition. AbstractSeparating a photograph into its reflectance and illumination intrinsic images is a fundamentally ambiguous problem, and state-of-the-art algorithms combine sophisticated reflectance and illumination priors with user annotations to create plausible results. However, these algorithms cannot be easily extended to videos for two reasons: first, naïvely applying algorithms designed for single images to videos produce results that are temporally incoherent; second, effectively specifying user annotations for a video requires interactive feedback, and current approaches are orders of magnitudes too slow to support this. We introduce a fast and temporally consistent algorithm to decompose video sequences into their reflectance and illumination components. Our algorithm uses a hybrid 2-p formulation that separates image gradients into smooth illumination and sparse reflectance gradients using look-up tables. We use a multi-scale parallelized solver to reconstruct the reflectance and illumination from these gradients while enforcing spatial and temporal reflectance constraints and user annotations. We demonstrate that our algorithm automatically produces reasonable results, that can be interactively refined by users, at rates that are two orders of magnitude faster than existing tools, to produce high-quality decompositions for challenging real-world video sequences. We also show how these decompositions can be used for a number of video editing applications including recoloring, retexturing, illumination editing, and lighting-aware compositing.

show abstract

Section: Introductionmentioning

confidence: 99%

Interactive intrinsic video editing

et al. 2014

View full text Add to dashboard Cite

show abstract

“…where ch(I p ) denotes the chromaticity of p. The left term expresses the well-established assumption that pixels that have similar chromaticity are likely to have similar albedo [14,12,36,15,21]. The right term is the geometric mean of the luminance values of p and q and attenuates the strength of the regularizer for darker pixels, for which the chromaticity is ill-conditioned.…”

Section: Regularizationmentioning

confidence: 99%

A Simple Model for Intrinsic Image Decomposition with Depth Cues

Chen

Koltun

2013

2013 IEEE International Conference on Computer Vision

170

147

View full text Add to dashboard Cite

We present a model for intrinsic decomposition of RGB-D images. Our approach analyzes a single RGB-D image and estimates albedo and shading fields that explain the input. To disambiguate the problem, our model estimates a number of components that jointly account for the reconstructed shading. By decomposing the shading field, we can build in assumptions about image formation that help distinguish reflectance variation from shading. These assumptions are expressed as simple nonlocal regularizers. We evaluate the model on real-world images and on a challenging synthetic dataset. The experimental results demonstrate that the presented approach outperforms prior models for intrinsic decomposition of RGB-D images.

show abstract

“…5 provides a visual comparison of our method against ground truth, as well as state-of-the-art automatic and user-assisted methods, all kindly provided by the authors of the previous work. In supplemental materials, we provide more images and comparisons to additional methods, including [Garces et al 2012] and our implementation of [Weiss 2001] extended to multiview, which is inspired by [Liu et al 2008]. In Fig.…”

Section: Intrinsic Decompositionsmentioning

confidence: 99%

Coherent intrinsic images from photo collections

et al. 2012

View full text Add to dashboard Cite

Figure 1: Our method leverages the heterogeneity of photo collections to automatically decompose photographs of a scene into reflectance and illumination layers. The extracted reflectance layers are coherent across all views, while the illumination captures the shading and shadow variations proper to each picture. Here we show the decomposition of three photos in the collection. AbstractAn intrinsic image is a decomposition of a photo into an illumination layer and a reflectance layer, which enables powerful editing such as the alteration of an object's material independently of its illumination. However, decomposing a single photo is highly under-constrained and existing methods require user assistance or handle only simple scenes. In this paper, we compute intrinsic decompositions using several images of the same scene under different viewpoints and lighting conditions. We use multi-view stereo to automatically reconstruct 3D points and normals from which we derive relationships between reflectance values at different locations, across multiple views and consequently different lighting conditions. We use robust estimation to reliably identify reflectance ratios between pairs of points. From these, we infer constraints for our optimization and enforce a coherent solution across multiple views and illuminations. Our results demonstrate that this constrained optimization yields high-quality and coherent intrinsic decompositions of complex scenes. We illustrate how these decompositions can be used for image-based illumination transfer and transitions between views with consistent lighting.

show abstract

Intrinsic Images by Clustering

Cited by 126 publications

References 26 publications

Interactive intrinsic video editing

Interactive intrinsic video editing

A Simple Model for Intrinsic Image Decomposition with Depth Cues

Coherent intrinsic images from photo collections

Contact Info

Product

Resources

About