FlatLands: Indoor floormap completion from a single egocentric view
FlatLands: Indoor floormap completion from a single egocentric view
This paper introduces FlatLands, a dataset and benchmark for single-view indoor floor-map completion. It contains 270,575 observations from 17,656 scene records across six metric 3D datasets. The study evaluates eleven models, including deterministic and generative methods, to assess floor-map accuracy and the representation of ambiguity in unseen space without fine-tuning.
The authors introduce FlatLands, a dataset and benchmark comprising 270,575 observations from 17,656 scene records across six metric 3D datasets, designed to evaluate eleven models for single-view indoor floor-map completion in metric bird's-eye view without fine-tuning.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Data origin
- Human egocentric dataOpen source S1
Reported numbers
observations
270,575
View original evidence
inistic 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 image
Open source S3datasets
6
View original evidence
inistic 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 image
Open source S3
- paper: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
FlatLands: Indoor floormap completion from a single egocentric view
Open source S1
inistic 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 image
Open source S3
arXiv:2603.16016v4 Announce Type: replace-cross 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 indoor floor-map completion in metric bird's-eye view (BEV), evaluating eleven models: both determ
Open source S2
s. 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
Open source S4
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Check whether the intended buyer needs human observations, hand or object annotations, or aligned robot actions. Human video alone does not provide robot commands.
- Compare the reported units and scope before using these quantities in a budget. Recording hours, sensor-hours and trajectories are different measures.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org