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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.16016 |
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| _version_ | 1866910055284080640 |
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| author | Bhattacharjee, Subhransu S. Campbell, Dylan Shome, Rahul |
| author_facet | Bhattacharjee, Subhransu S. Campbell, Dylan Shome, Rahul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16016 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FlatLands: Generative Floormap Completion From a Single Egocentric View Bhattacharjee, Subhransu S. Campbell, Dylan Shome, Rahul Computer Vision and Pattern Recognition Artificial Intelligence Robotics Image and Video Processing 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. |
| title | FlatLands: Generative Floormap Completion From a Single Egocentric View |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2603.16016 |