Datasets:

astralhf
/

yonder

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	"@type": "sc:Dataset",
	"name": "Yonder",
	"description": "A 4.65-million-frame drone-perspective dataset spanning 167 indoor 3D environments (all from HSSD, all with semantic annotations), with stereo RGB, depth, IR, LiDAR-360, and semantic segmentation captured at 387,527 navmesh-sampled waypoints (12 yaws each). Released alongside a closed-loop benchmark designed to expose the cross-simulator generalization gap: the failure of perception trained on one simulator to transfer to a different simulator, even when both target the same task.",
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	"license": "https://creativecommons.org/licenses/by-nc/4.0/",
	"url": "https://huggingface.co/datasets/astralhf/yonder",
	"version": "1.0.0",
	"datePublished": "2026-05-01",
	"creator": {
	"@type": "Organization",
	"name": "Anonymous (withheld for NeurIPS 2026 double-blind review)"
	},
	"publisher": {
	"@type": "Organization",
	"name": "Anonymous (withheld for NeurIPS 2026 double-blind review)"
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	"keywords": [
	"robotics",
	"drone navigation",
	"vision-language navigation",
	"open-vocabulary detection",
	"embodied AI",
	"Habitat-Sim",
	"Isaac Sim",
	"cross-simulator transfer",
	"closed-loop benchmark"
	],
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	"citeAs": "@inproceedings{anonymous2026yonder, title={Yonder: A 4.65M-Frame Drone Navigation Dataset and the Cross-Simulator Generalization Gap}, author={Anonymous Author(s)}, booktitle={NeurIPS Datasets and Benchmarks Track}, year={2026}}",

	"rai:dataCollection": "Generated by flying a simulated Holybro x500v2 quadcopter through 3D scenes in Habitat-Sim. Waypoint locations are sampled from the scene navmesh with adaptive densification; 12 yaw orientations are captured per waypoint. Sensor capture is fully deterministic given (scene_id, seed). No human subjects were involved in any phase of data collection. No real-world imagery was collected.",
	"rai:dataCollectionType": "simulated",
	"rai:dataCollectionTimeFrame": "2026-01-01/2026-03-31",
	"rai:dataAnnotationProtocol": "Semantic segmentation annotations are obtained directly from the source 3D scene meshes (HSSD). Each pixel inherits the instance ID of the underlying mesh face. Bounding boxes are derived programmatically: for each unique semantic instance ID with rendered area > 100 pixels, the axis-aligned bounding box of its pixel set is recorded. All 167 HSSD scenes carry semantic instance labels (median 38 unique instances per scene, range 14-80). No human annotators were involved.",
	"rai:dataAnnotationPlatform": "n/a (programmatic, no human annotators)",
	"rai:dataAnnotationAnalysis": "Bounding box quality was spot-checked across 50 randomly sampled waypoints from 10 scenes. Common failure modes: (1) over-segmentation of articulated objects (e.g., a chair's legs become separate boxes from its seat where mesh authoring split them); (2) tight bounding boxes around partially-occluded objects can be visually misleading. These are inherent to mesh-authored semantics and are documented as a known limitation.",
	"rai:dataReleaseMaintenancePlan": "The dataset is released as a static v1.0.0 snapshot. Errata, additional split files, and supplementary metadata may be added under semver patch/minor releases; the rendered NPZ data itself will not be modified post-release. Issues and clarifications are tracked on the HuggingFace Hub repository. After NeurIPS deanonymization, a maintainer contact will be added to the dataset card.",
	"rai:dataLimitations": "Yonder is a synthetic-only dataset. Performance on Yonder does not transfer to real-world imagery without explicit sim-to-real treatment. The source scenes (HSSD) are biased toward Western residential interiors; transfer to other interior styles, outdoor settings, or real-world drone footage is not validated. Semantic annotations come from HSSD's mesh-authored instance IDs and inherit known limitations (over-segmentation of articulated objects; occasionally misleading boxes around partially-occluded instances).",
	"rai:hasSyntheticData": true,
	"prov:wasGeneratedBy": "Rendered in Habitat-Sim 0.3.x using a simulated Holybro x500v2 quadcopter. Waypoints were sampled from each scene's navmesh with adaptive density; 12 yaw orientations were captured per waypoint across stereo RGB, depth, IR, LiDAR-360, and semantic-segmentation sensors. Rendering is fully deterministic given (scene_id, seed). Source 3D scenes are HSSD (167 scenes). No human subjects, no real imagery, no PII.",
	"rai:dataSocialImpact": "Drone-perspective perception models trained on this dataset could in principle be applied to surveillance, person-identification, or autonomous-targeting systems. Yonder contains no real persons or PII, but the perception capabilities it enables are dual-use. We discourage use of Yonder-trained models for any application that identifies specific real persons or supports lethal-autonomy systems. The CC-BY-NC license also restricts commercial military/surveillance applications by default.",
	"rai:dataBiases": "Scene-distribution bias: all scenes are HSSD residential interiors (kitchens, bedrooms, living rooms with Western design conventions). Underrepresented: institutional/industrial interiors, non-Western residential styles, outdoor or transitional spaces, low-light or unusual-lighting conditions. Object-distribution bias: object categories are skewed toward those well-represented in HSSD's mesh library (furniture, common household objects); rare or specialized objects are underrepresented. Geographic bias: scene authors and 3D-scan subjects are concentrated in North America and Western Europe.",
	"rai:dataUseCases": "Recommended: training drone-perspective perception models with closed-loop validation in the deployment simulator; studying cross-simulator generalization; benchmarking visual-language navigation when paired with a closed-loop evaluator. Discouraged: end-to-end navigation policy training (no expert trajectories provided); reporting fine-tuning gains based solely on Yonder's offline evaluation split. Disallowed: surveillance, biometric identification, or any application that identifies specific real persons.",
	"rai:personalSensitiveInformation": "None. Yonder is rendered from synthetic 3D scenes with no real persons, no faces, no PII, and no biometric data. The simulated drone trajectories contain only synthetic poses and sensor readings.",

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