FlatLands: Generative Floormap Completion From a Single Egocentric View

[Submitted on 16 Mar 2026 (v1), last revised 27 Jun 2026 (this version, v2)]

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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.

Submission history

From: Subhransu S. Bhattacharjee Mr. [view email]
[v1] Mon, 16 Mar 2026 23:50:16 UTC (7,095 KB)
[v2] Sat, 27 Jun 2026 05:45:45 UTC (7,145 KB)