The paper first outlines the development and integration of a helicopter-ship dynamic interface model into a wide field-of-view, fixed-base rotorcraft simulation environment. The present research seeks to develop novel visual and control augmentation techniques and determine their impact on human factors for shipboard recovery operations. Shipboard launch and recovery of helicopters continues to pose operational challenges even to experienced pilots. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using conventional non-rigid structure from motion approaches and state-of-the-art video depth estimation techniques. We address this ambiguity problem by employing the invariant-theoretic normalization procedure in order to obtain complete invariants with respect to this group of transformations, and use them in the loss function of a neural network. These are known in the literature as generalized bas-relief (GBR) transformations. Since the database images are rendered with an orthographic camera model, linear deformations along the optical axis cannot be recovered from the training images.
![renderman water renderman water](https://sdm.scad.edu/faculty/mkesson/vsfx319/wip/best/best_winter2004/ca301.3/erik_zimmermann/renderman/water/nemo1.jpg)
#RENDERMAN WATER PATCH#
Since the geometric complexity of a local spatiotemporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network.
#RENDERMAN WATER FULL#
The estimation of depth is performed locally on spatiotemporal patches of the video, and then, the full depth video is recovered by combining them together. We present a method to locally reconstruct dense video depth maps of a non-rigidly deformable object directly from a video sequence acquired by a static orthographic camera. To improve the precision of automatically detecting concrete cracks on underwater surfaces, new optical effect augmentation techniques have been developed. This research elaborates on difficulties encountered when using deep learning-based techniques to identify concrete cracks when optical effects are present.
![renderman water renderman water](https://bloximages.chicago2.vip.townnews.com/nwitimes.com/content/tncms/assets/v3/editorial/a/60/a60b0a39-2e57-551f-96ab-7268e3625429/55a82d64c63b3.image.jpg)
#RENDERMAN WATER CRACK#
In this research, we investigate the challenging underwater optical effects settings and the complexity of image classification for concrete crack detection. The focus of the published research and accessible shadow databases is on photographs shot in controlled laboratory settings. Complex lighting situations, shadows, the irrationality of crack forms and widths, imperfections, and concrete spall frequently have an influence on real-world photos. Although deep learning-based systems assert to have extremely high accuracy, they frequently overlook how difficult it is to acquire images. Convolutional neural networks have been created as deep learning-based approaches to automatically analyze photographs of concrete surfaces for crack diagnosis applications.