The loss functionof Mask-MCNet is similar to Mask R-CNN with an equal contribution of three terms. The first term is a cross-entropy loss value for the classification branch on its softmax output layer. The second term is a mean squared error at the linear output layer of the regression...
In the architecture of Mask-MCNet, the Monte Carlo ConvNet transfers the information from the point cloud where it is spread over the surface of objects into the entire 3D space (e.g. the void space inside of objects). This property facilitates the inference on the center and size of obj...
(MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points ...
3D point cloudInstance segmentationIntra-oral scanAccurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for......
The MCNet approach adjusts the discrepancy between the quality of the mask and its proposed detection, enhancing the effectiveness of instance segmentation. The final results for two datasets trained using PBC and BCCD are as follows: the accuracy of mAP@IoU0.50 for...