The combination of a pattern projector and a camera is widely used for 3D measurement. To recover shape from a captured image, various kinds of depth cues are extracted from projected patterns in the image, such as disparities from active stereo or blurriness for depth from defocus. Recently, several techniques have been proposed to improve 3D quality using multiple depth cues by installing coded apertures in projectors or by increasing the number of projectors. However, superposition of projected patterns forms a complicated light field in 3D space, which makes the process of analyzing captured images challenging. In this paper, we propose a learning-based technique to extract depth information from such a light field, which includes multiple depth cues. In the learning phase, prior to the 3D measurement of unknown scenes, projected patterns as they appear at various depths are prepared from not only actual images but also ones generated virtually using computer graphics and geometric calibration results. Then, we use principal component analysis (PCA) to extract features of small patches. In the 3D measurement (reconstruction) phase, the same features of patches are extracted from a captured image of a target scene and compared with the learned data. By using the dimensional reduction by feature extraction, an efficient search algorithm, such as an approximated nearest neighbor (ANN), can be used for the matching process. Another important advantage of our learning-based approach is that we can use most known projection patterns without changing the algorithm.
|Journal||IPSJ Transactions on Computer Vision and Applications|
|Publication status||Published - Dec 1 2019|
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition