Such a thing The authors of the article give ORBSLAM as an example . For each fragment base on the frames include. In it a bounding cubic volume with a fixe maximum depth. Maxor FBV fragment bounding volume is found. For better understanding I circle FBV in re Reconstruct able FBV scene Reconstruct able FBV scene Construction of a thre eimensional. Feature map Next comes a block highlighting features of fragment images. In our case the authors took MnasNet a lightweight architecture for mobile. Phones as a backbone model so as not to suffer in terms of performance.
It is important to note that the features
Are allocate pyramidally at the output from MnasNet we have three levels of features of different scales as can be seen in the diagram. After the previous stage with feature Australia Phone Number List extraction. We take the last level and begin to gradually expand it. In parallel we perform the socalle backprojection or unprojection thereby obtaining volumetric representations of features Fti. Back projection Lets look at back projection in more detail.
This concept first appeare in compute
Tomography and means an algorithm for image reconstruction base on readings from radiation sensors obtaine from different angles. Lets say we have a certain object in the form of a table of numbers through which a ray passes summing the values along the way The resulting total values are the readings from the sensors The resulting total values are the readings from the sensors Australia Email List But what if base on sensor readings we now want to restore the original object.
