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In this paper, we propose and showcase, for the first time, monocularmulti-view layout estimation for warehouse racks and shelves. Unlike typicallayout estimation methods, MVRackLay estimates multi-layered layouts, whereineach layer corresponds to the layout of a shelf within a rack. Given a sequenceof images of a warehouse scene, a dual-headed Convolutional-LSTM architectureoutputs segmented racks, the front and the top view layout of each shelf withina rack. With minimal effort, such an output is transformed into a 3D renderingof all racks, shelves and objects on the shelves, giving an accurate 3Ddepiction of the entire warehouse scene in terms of racks, shelves and thenumber of objects on each shelf. MVRackLay generalizes to a diverse set ofwarehouse scenes with varying number of objects on each shelf, number ofshelves 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 mAPmetrics. We also showcase a multi-view stitching of the 3D layouts resulting ina representation of the warehouse scene with respect to a global referenceframe akin to a rendering of the scene from a SLAM pipeline. To the best of ourknowledge, this is the first such work to portray a 3D rendering of a warehousescene in terms of its semantic components - Racks, Shelves and Objects - allfrom a single monocular camera.

MVRackLay: Monocular Multi-View Layout Estimation for Warehouse Racks and Shelves