  In this paper, we propose and showcase, for the first time, monocular
multi-view layout estimation for warehouse racks and shelves. Unlike typical
layout estimation methods, MVRackLay estimates multi-layered layouts, wherein
each layer corresponds to the layout of a shelf within a rack. Given a sequence
of images of a warehouse scene, a dual-headed Convolutional-LSTM architecture
outputs segmented racks, the front and the top view layout of each shelf within
a rack. With minimal effort, such an output is transformed into a 3D rendering
of all racks, shelves and objects on the shelves, giving an accurate 3D
depiction of the entire warehouse scene in terms of racks, shelves and the
number of objects on each shelf. MVRackLay generalizes to a diverse set of
warehouse scenes with varying number of objects on each shelf, number of
shelves and in the presence of other such racks in the background. Further,
MVRackLay shows superior performance vis-a-vis its single view counterpart,
RackLay, in layout accuracy, quantized in terms of the mean IoU and mAP
metrics. We also showcase a multi-view stitching of the 3D layouts resulting in
a representation of the warehouse scene with respect to a global reference
frame akin to a rendering of the scene from a SLAM pipeline. To the best of our
knowledge, this is the first such work to portray a 3D rendering of a warehouse
scene in terms of its semantic components - Racks, Shelves and Objects - all
from a single monocular camera.
