This Neural A set of N key frames and their corresponding camera positions is define as a local fragment I_txi_t_N where I_tis the RGB image from the camera at time t and xi_tis the corresponding camera position at time t. A reasonable question arises How do we know the position of the camera in space xi_tif we only have an RGB camera and no IMU sensors The fact is that in this method another SLAM algorithm is use in parallel tailore specifically to determine the relative location of the camera from monocular video.
Lets move on to consider
The diagram of this wonderful model D FORMER diagram D FORMER diagram Doesnt the diagram remind you of anything Yes this is the same Neural Recon only with Brazil Phone Number List transformers instead of recurrent blocks We also select levels of features for a fragment project them into volumetric representations and transfer the whole thing to the CoarseToFine module to generate the final TSDF. It is worth noting that the mechanism for obtaining a volumetric representation for all projections works.
A little differently we take not just
The average over all transparency values of the projection voxels but a weighte average the weights for which we obtain from a small pretraine MLP. Thus the greater Brazil Email List contribution comes from those projections that will best influence the final quality of the reconstruction. We will consider the CoarseToFine block with transformers separately CoarseToFine Transformer.
