Abstract
The photometric stereo method enables estimation of surface normals from images that have been captured using different but known lighting directions. The classical photometric stereo method requires at least three images to determine the normals in a given scene. However, this method cannot be applied to dynamic scenes because it is assumed that the scene remains static while the required images are captured. In this work, we present a dynamic photometric stereo method for estimation of the surface normals in a dynamic scene. We use a multi-tap complementary metal-oxide-semiconductor (CMOS) image sensor to capture the input images required for the proposed photometric stereo method. This image sensor can divide the electrons from the photodiode from a single pixel into the different taps of the exposures and can thus capture multiple images under different lighting conditions with almost identical timing. We implemented a camera lighting system and created a software application to enable estimation of the normal map in real time. We also evaluated the accuracy of the estimated surface normals and demonstrated that our proposed method can estimate the surface normals of dynamic scenes.
Highlights
These methods can be separated into two types: active sensing methods and passive sensing methods.Passive sensing methods such as stereo vision techniques use only image sensors and capture the surfaces of a target scene from different viewpoints
We propose a dynamic photometric stereo method based on use of a multi-tap complementary metal-oxide-semiconductor (CMOS) image sensor
Equation (5) indicates that the photometric stereo method requires at least three images that have been captured under different lighting directions to calculate the surface normals because L† should be a matrix of rank 3
Summary
These methods can be separated into two types: active sensing methods and passive sensing methods. The camera and the scene should both remain static while all three input images are captured, which means that the pixel intensities of the images are only affected by changes in the lighting because each of the pixels in the images should correspond to the same scene points in each image Under these assumptions, the photometric stereo method provides linear estimates of the normal vector for each pixel based on the intensities of the three images. The pixels in the different images are still assumed to correspond and the photometric stereo method can be used to estimate the surface normals Such high-speed cameras are generally expensive, and the images have lower signal-to-noise ratios (SNRs) because the high-speed camera uses shorter exposure times to produce higher frame rates. This paper is an extended version of our earlier conference paper on this system [13]
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