  Given a monocular colour image of a warehouse rack, we aim to predict the
bird's-eye view layout for each shelf in the rack, which we term as multi-layer
layout prediction. To this end, we present RackLay, a deep neural network for
real-time shelf layout estimation from a single image. Unlike previous layout
estimation methods, which provide a single layout for the dominant ground plane
alone, RackLay estimates the top-view and front-view layout for each shelf in
the considered rack populated with objects. RackLay's architecture and its
variants are versatile and estimate accurate layouts for diverse scenes
characterized by varying number of visible shelves in an image, large range in
shelf occupancy factor and varied background clutter. Given the extreme paucity
of datasets in this space and the difficulty involved in acquiring real data
from warehouses, we additionally release a flexible synthetic dataset
generation pipeline WareSynth which allows users to control the generation
process and tailor the dataset according to contingent application. The
ablations across architectural variants and comparison with strong prior
baselines vindicate the efficacy of RackLay as an apt architecture for the
novel problem of multi-layered layout estimation. We also show that fusing the
top-view and front-view enables 3D reasoning applications such as metric free
space estimation for the considered rack.
