Signe Distance After GRU Fusion the hidden state H_tlgoes to the input of a regular MLP which preicts a new TSDF S_tiat the current level of detail. This TSDF is then upsample and concatenate with the volumetric feature map of the next level until it reaches the final TSDF S_tl. Model training Before finishing the analysis lets take a look at the NeuralRecon training process. It contains loss functions Error for preicte voxel mesh. As is known from previous articles in the series it is calculate using binary crossentropy.
Error for SDF function values
Found using the L distance between the true and preicte value. In this case datasets were use ScanNet V a set of indoor scenes with specifie camera positions semantic segmentation UK Phone Number List and surface reconstruction. Use for model training and validation. Scenes a dataset consisting of RGBD images of enclose spaces. Use solely for model validation. D FORMER NeuralRecons main drawback namely its low reconstruction accuracy stems in part from the use of recurrent neural networks at the core of its architecture.
In this historical period recurrent
Networks gave way to transformer models the same transformers that gave an unpreceente impetus to the development of generative models detection models and indee Philippines Whatsapp Number List the entire field of artificial intelligence. In this regard the most obvious optimization comes to mind lets use transformers. Actually this is what our next model does D FORMER. Improvement in the quality of reconstruction is obvious At the same time the operating spee decrease by almost times.
