International Journal of Computer Vision (IJCV), 97(1), 18–35.Īanæs, H., Jensen, R. Interesting interest points-A comparative study of interest point performance on a unique data set.
#2d objects using nodebox 3 code#
Data and test code are available at the project page.Īanæs, H., Dahl, A. Our approach also outperforms the competition on this dataset as well. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world ’dataset for near-fieLd point light soUrCe photomEtric Stereo’ of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. Especially they ignore specular reflection and global illumination effects. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered.
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