← 研踪 PaperTrailarXiv:2603.16016 · v4
FlatLands: Indoor floormap completion from a single egocentric view
首次提交 2026-03-17 07:50
版本更新 2026-11-03 08:11
雷达观测 2026-11-04 23:40
摘要
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 indoor floor-map completion in metric bird's-eye view (BEV), evaluating eleven models: both deterministic and generative. FlatLands contain 270,575 observations from 17,656 scene records across six metric 3D datasets, with aligned observation, visibility, validity, and ground-truth maps and both in- and out-of-distribution protocols. We compare training-free, deterministic, ensemble, and generative methods on shared geometry-derived inputs and, without fine-tuning, on maps estimated from real egocentric RGB images. Our results show that predicting floor almost everywhere can achieve high IoU when most evaluated cells are floor, while generative sample sets outperform the best single-output model under a proper scoring rule and support safer risk-weighted planning. FlatLands establishes a rigorous testbed for evaluating both floor-map accuracy and how effectively alternative completions represent the ambiguity of unseen space.
作者备注：38 pages, 18 figures. Under review. Updated manuscript, title, abstract, and experiments
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v4 · 2026-11-03 08:11 · 最新观测版本
FlatLands: Indoor floormap completion from a single egocentric view
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 indoor floor-map completion in metric bird's-eye view (BEV), evaluating eleven models: both deterministic and generative. FlatLands contain 270,575 observations from 17,656 scene records across six metric 3D datasets, with aligned observation, visibility, validity, and ground-truth maps and both in- and out-of-distribution protocols. We compare training-free, deterministic, ensemble, and generative methods on shared geometry-derived inputs and, without fine-tuning, on maps estimated from real egocentric RGB images. Our results show that predicting floor almost everywhere can achieve high IoU when most evaluated cells are floor, while generative sample sets outperform the best single-output model under a proper scoring rule and support safer risk-weighted planning. FlatLands establishes a rigorous testbed for evaluating both floor-map accuracy and how effectively alternative completions represent the ambiguity of unseen space.
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