Monocular Multi-Layer Layout Estimation for Warehouse Racks
This addresses a novel problem in warehouse automation by enabling detailed layout estimation for racks, though it is incremental in applying layout estimation to a specific domain.
The paper tackles the problem of predicting bird's-eye view layouts for each shelf in warehouse racks from monocular images, introducing RackLay, a deep neural network that achieves real-time, accurate multi-layer layout estimation, including top-view and front-view predictions, and demonstrates applications like 3D free space estimation.
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.