FlatLands: Generative Floormap Completion From a Single Egocentric View
Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome

TL;DR
FlatLands introduces a comprehensive dataset and benchmark for completing full indoor floor maps from a single egocentric view, enabling better indoor navigation and mapping applications.
Contribution
The paper presents a new dataset, benchmark, and an end-to-end RGB-to-floor map pipeline for single-view indoor floor completion tasks.
Findings
Comparison of various approaches including training-free, deterministic, ensemble, and generative models.
FlatLands dataset contains over 270,000 observations from diverse indoor scenes.
Benchmark facilitates evaluation of uncertainty-aware indoor mapping methods.
Abstract
A single egocentric image typically captures only a small portion of the floor, yet a complete metric traversability map of the surroundings would better serve applications such as indoor navigation. We introduce FlatLands, a dataset and benchmark for single-view bird's-eye view (BEV) floor completion. The dataset contains 270,575 observations from 17,656 real metric indoor scenes drawn from six existing datasets, with aligned observation, visibility, validity, and ground-truth BEV maps, and the benchmark includes both in- and out-of-distribution evaluation protocols. We compare training-free approaches, deterministic models, ensembles, and stochastic generative models. Finally, we instantiate the task as an end-to-end monocular RGB-to-floormaps pipeline. FlatLands provides a rigorous testbed for uncertainty-aware indoor mapping and generative completion for embodied navigation.
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Vision and Imaging · Remote Sensing and LiDAR Applications
