Deep Learning Photometric Stereo
GPTKB entity
Statements (24)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
computer vision technique
|
| gptkbp:application |
3D reconstruction
surface normal estimation |
| gptkbp:challenge |
generalization to real-world data
non-Lambertian surfaces uncalibrated lighting |
| gptkbp:field |
gptkb:machine_learning
computer vision |
| gptkbp:firstPapers |
2017
|
| https://www.w3.org/2000/01/rdf-schema#label |
Deep Learning Photometric Stereo
|
| gptkbp:input |
images under varying illumination
|
| gptkbp:notablePublication |
PS-FCN: A Flexible Learning Framework for Photometric Stereo (2018)
Uncalibrated Photometric Stereo with Deep Spatially-Varying BRDFs (2018) Self-calibrating Deep Photometric Stereo Networks (2019) |
| gptkbp:output |
surface normals
|
| gptkbp:relatedTo |
convolutional neural networks
neural networks classical photometric stereo |
| gptkbp:solvedBy |
photometric stereo problem
|
| gptkbp:trainer |
DiLiGenT
MERL BRDF database |
| gptkbp:uses |
deep learning
|
| gptkbp:bfsParent |
gptkb:Photometric_Stereo
|
| gptkbp:bfsLayer |
7
|