18 research outputs found
An L1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition
Identifying sparse salient structures from dense pixels is a longstanding problem in visual computing. Solutions to this problem can benefit both image manipulation and understanding. In this paper, we introduce an image transform based on the L1 norm for piecewise image flattening. This transform can effectively preserve and sharpen salient edges and contours while eliminating insignificant details, producing a nearly piecewise constant image with sparse structures. A variant of this image transform can perform edge-preserving smoothing more effectively than existing state-of-the-art algorithms. We further present a new method for complex scene-level intrinsic image decomposition. Our method relies on the above image transform to suppress surface shading variations, and perform probabilistic reflectance clustering on the flattened image instead of the original input image to achieve higher accuracy. Extensive testing on the Intrinsic-Images-in-the-Wild database indicates our method can perform significantly better than existing techniques both visually and numerically. The obtained intrinsic images have been successfully used in two applications, surface retexturing and 3D object compositing in photographs.postprin
CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering
Intrinsic image decomposition is a challenging, long-standing computer vision
problem for which ground truth data is very difficult to acquire. We explore
the use of synthetic data for training CNN-based intrinsic image decomposition
models, then applying these learned models to real-world images. To that end,
we present \ICG, a new, large-scale dataset of physically-based rendered images
of scenes with full ground truth decompositions. The rendering process we use
is carefully designed to yield high-quality, realistic images, which we find to
be crucial for this problem domain. We also propose a new end-to-end training
method that learns better decompositions by leveraging \ICG, and optionally IIW
and SAW, two recent datasets of sparse annotations on real-world images.
Surprisingly, we find that a decomposition network trained solely on our
synthetic data outperforms the state-of-the-art on both IIW and SAW, and
performance improves even further when IIW and SAW data is added during
training. Our work demonstrates the suprising effectiveness of
carefully-rendered synthetic data for the intrinsic images task.Comment: Paper for 'CGIntrinsics: Better Intrinsic Image Decomposition through
Physically-Based Rendering' published in ECCV, 201
Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation
Intrinsic image decomposition and inverse rendering are long-standing
problems in computer vision. To evaluate albedo recovery, most algorithms
report their quantitative performance with a mean Weighted Human Disagreement
Rate (WHDR) metric on the IIW dataset. However, WHDR focuses only on relative
albedo values and often fails to capture overall quality of the albedo. In
order to comprehensively evaluate albedo, we collect a new dataset, Measured
Albedo in the Wild (MAW), and propose three new metrics that complement WHDR:
intensity, chromaticity and texture metrics. We show that existing algorithms
often improve WHDR metric but perform poorly on other metrics. We then finetune
different algorithms on our MAW dataset to significantly improve the quality of
the reconstructed albedo both quantitatively and qualitatively. Since the
proposed intensity, chromaticity, and texture metrics and the WHDR are all
complementary we further introduce a relative performance measure that captures
average performance. By analysing existing algorithms we show that there is
significant room for improvement. Our dataset and evaluation metrics will
enable researchers to develop algorithms that improve albedo reconstruction.
Code and Data available at: https://measuredalbedo.github.io/Comment: Accepted into ICCP202