Firstly we train a per-pixel CNN to predict surface nor- mals from reflectance samples. 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.
Sep 12, 2020 · In this work, we propose the first CNN based approach capable of handling these realistic assumptions in Photometric Stereo.
Oct 13, 2020 · In this work, we propose the first CNN based approach capable of handling these realistic assumptions in Photometric Stereo. We leverage recent ...
Oct 10, 2022 · Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal ...
This is an embedded video. Talk and the respective paper are published at BMVC 2020 virtual conference. If you are one of the authors of the paper and want to ...
In order to fill the gap in evaluating near-field photometric stereo methods, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe ...
[PDF] A CNN Based Approach for the Point-Light Photometric Stereo ...
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This work proposes a CNN-based approach capable of handling realistic assumptions by leveraging recent improvements of deep neural networks for far-field ...
In this work, we propose the first CNN based approach capable of handling these realistic assumptions in Photometric Stereo. We leverage recent improvements of ...
Abstract summary: We propose the first CNN based approach capable of handling realistic assumptions in Photometric Stereo. We leverage recent improvements of ...
In this work, we present a volumetric approach to the multi-view photometric stereo problem. 3D Volumetric Reconstruction · Paper