Median Photometric Stereo as Applied to the Segonko Tumulus and Museum Objects

Miyazaki, Daisuke; Hara, Kenji; Ikeuchi, Katsushi

doi:10.1007/s11263-009-0262-9

Cited by 71 publications

(33 citation statements)

References 72 publications

(60 reference statements)

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“…While our method is slower than the LS approach, it is much more accurate in a wide variety of scenarios and is more efficient than other existing methods (e.g. [10]). …”

Section: Quantitative Evaluation With Synthetic Imagesmentioning

confidence: 93%

“…More importantly, our method is able to use all the available information simultaneously for obtaining the optimal result, instead of discarding informative measurements, e.g., by either selecting the best set of illumination directions [9] or using the median estimator [10].…”

Section: Contributionsmentioning

confidence: 99%

“…We recall that λ is a weighting parameter in our formulation given by Eq. (10). In all the above experiments, we have fixed the value of the parameter λ = 1/ √ m, as suggested by [14].…”

Section: Quantitative Evaluation With Synthetic Imagesmentioning

confidence: 99%

“…The core computation of our method is solving a convex program Eq. (10). For the specular Caesar data ( Fig.…”

Section: Quantitative Evaluation With Synthetic Imagesmentioning

confidence: 99%

“…To overcome this problem, researchers have explored various approaches to eliminate such deviations by treating the corrupted measurements as outliers, e.g., using a RANSAC scheme [8,9], or a median-based approach [10]. To identify the different types of corruptions in images more carefully, Mukaiegawa et al .…”

Section: Introductionmentioning

confidence: 99%

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Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery

Ganesh

Shi

et al. 2011

Computer Vision – ACCV 2010

207

240

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Abstract. We present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recovering a low-rank matrix with both missing entries and corrupted entries, which model all types of non-Lambertian effects such as shadows and specularities. Unlike previous approaches that use Least-Squares or heuristic robust techniques, our method uses advanced convex optimization techniques that are guaranteed to find the correct low-rank matrix by simultaneously fixing its missing and erroneous entries. Extensive experimental results demonstrate that our method achieves unprecedentedly accurate estimates of surface normals in the presence of significant amount of shadows and specularities. The new technique can be used to improve virtually any photometric stereo method including uncalibrated photometric stereo.

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“…While our method is slower than the LS approach, it is much more accurate in a wide variety of scenarios and is more efficient than other existing methods (e.g. [10]). …”

Section: Quantitative Evaluation With Synthetic Imagesmentioning

confidence: 93%

Section: Contributionsmentioning

confidence: 99%

Section: Quantitative Evaluation With Synthetic Imagesmentioning

confidence: 99%

“…The core computation of our method is solving a convex program Eq. (10). For the specular Caesar data ( Fig.…”

Section: Quantitative Evaluation With Synthetic Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery

Ganesh

Shi

et al. 2011

Computer Vision – ACCV 2010

207

240

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PS-FCN: A Flexible Learning Framework for Photometric Stereo

Chen

Han

Wong

2018

Computer Vision – ECCV 2018

117

189

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This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo. Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.

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