# DAVIDSTUTZ

Meet me at CVPR'18: Tuesday, June 19th, I will be presenting our work on weakly-supervised 3D shape completion.
10thMARCH2018

In general, octrees are implemented using pointers, i.e. a block at level $l$ contains pointers to the contained blocks at level $l + 1$, if the block is subdivided. However, Tatarchenko, similar to Riegler et al. [25], choose to organize the octree in a hash-table for efficient access. Then, as in Figure 1, they define three different operations: a convolution of OGNs, a loss on OGNs and a propagation scheme between levels. In general, an OGN starts by predicting the coarsest level using a convolution implemented on octree. Then, the generated octree is compared to the ground truth octree after predicting the state of the blocks – i.e. empty, filled or mixed. The first two states correspond to an occupancy value (0 or 1) while the latter means that this block is refined in later layers. The loss can be defined on the classification of the individual blocks. The blocks classified as “mixed” are further refined. A propagation layer allows to propagate the “mixed” blocks as well as it neighbors and the process is repeated at a more detailed level. This last step can either be guided by the ground truth octree (known shape) or the predicted octree (unkown shape).