  Single-view RGB-D human reconstruction with implicit functions is often
formulated as per-point classification. Specifically, a set of 3D locations
within the view-frustum of the camera are first projected independently onto
the image and a corresponding feature is subsequently extracted for each 3D
location. The feature of each 3D location is then used to classify
independently whether the corresponding 3D point is inside or outside the
observed object. This procedure leads to sub-optimal results because
correlations between predictions for neighboring locations are only taken into
account implicitly via the extracted features. For more accurate results we
propose the occupancy planes (OPlanes) representation, which enables to
formulate single-view RGB-D human reconstruction as occupancy prediction on
planes which slice through the camera's view frustum. Such a representation
provides more flexibility than voxel grids and enables to better leverage
correlations than per-point classification. On the challenging S3D data we
observe a simple classifier based on the OPlanes representation to yield
compelling results, especially in difficult situations with partial occlusions
due to other objects and partial visibility, which haven't been addressed by
prior work.
