Datasets:
Modalities:
Text
Languages:
English
Size:
< 1K
Tags:
robotics
drone-navigation
vision-language-navigation
open-vocabulary-detection
embodied-ai
habitat-sim
License:
| license: cc-by-nc-4.0 | |
| tags: | |
| - robotics | |
| - drone-navigation | |
| - vision-language-navigation | |
| - open-vocabulary-detection | |
| - embodied-ai | |
| - habitat-sim | |
| - isaac-sim | |
| - benchmark | |
| task_categories: | |
| - object-detection | |
| - depth-estimation | |
| - robotics | |
| size_categories: | |
| - 100K<n<1M | |
| pretty_name: "Yonder: A 4.65M-Frame Drone Navigation Dataset" | |
| language: | |
| - en | |
| # Yonder: A 4.65M-Frame Drone-Perspective Dataset for Indoor Navigation | |
| > **The cross-simulator generalization gap.** | |
| > Yonder is the largest publicly available drone-perspective dataset for indoor | |
| > navigation, plus a closed-loop benchmark designed to expose a failure mode invisible | |
| > to standard offline metrics: perception trained on one simulator does not transfer | |
| > cleanly to a different simulator, even when both target the same task. | |
| This dataset accompanies the NeurIPS 2026 Datasets & Benchmarks submission: | |
| *"Yonder: A 4.65M-Frame Drone Navigation Dataset and the Cross-Simulator Generalization Gap."* | |
| ## Headline numbers (paper subset) | |
| - **4,650,324** drone-perspective frames | |
| - **387,527** waypoint NPZ files (one per waypoint × 12 yaws) | |
| - **167** indoor 3D environments (all from HSSD, all with semantic annotations) | |
| - **52 sensor arrays** per NPZ (stereo RGB, depth, IR, LiDAR-360, semantic segmentation, pose, IMU) | |
| - **~3.3 TB** total | |
| ## What's in a waypoint | |
| Every waypoint NPZ contains a single drone pose with **12 yaw orientations**. For each yaw: | |
| | Sensor | Resolution / Format | | |
| |---|---| | |
| | Left RGB | 640×480, uint8 | | |
| | Right RGB | 640×480, uint8 | | |
| | Forward depth | 640×480, float16 (meters) | | |
| | Landing camera | 640×480, uint8 (downward) | | |
| | Up IR / Down IR | 640×480, uint8 | | |
| | LiDAR-360 | 1024 × 16 channel, float32 (meters) | | |
| | Position / Orientation / IMU | float32 (Habitat-Sim world frame) | | |
| | Semantic segmentation | 640×480 instance + class IDs (all 167 scenes) | | |
| ## Source environments | |
| Yonder is rendered from a single open-source 3D scene dataset: | |
| | Source | License | Scenes | Waypoints | Has Semantics | | |
| |---|---|---:|---:|---| | |
| | [HSSD](https://huggingface.co/datasets/hssd/hssd-hab) (Habitat Synthetic Scenes Dataset) | CC-BY-NC-4.0 | 167 | 387,527 | 167 of 167 | | |
| > Earlier collection passes also covered ReplicaCAD (84 scenes, CC-BY-4.0, no semantics), | |
| > Replica (Meta research-only terms), and HM3D (Matterport academic EULA). Replica and | |
| > HM3D were excluded because their upstream licenses do not permit open redistribution | |
| > of derivative renders. ReplicaCAD was excluded because it lacks semantic annotations | |
| > and was not used in any reported experiment, in service of a single-source, | |
| > fully-experiment-relevant artifact. **Yonder ships its own rendered observations only — | |
| > no upstream meshes are redistributed.** | |
| ## Preview gallery | |
| A curated frame from each of the **227 scenes** (167 HSSD-derived | |
| indoor + 60 Isaac-sim-native). Frames are picked from each scene's actual NPZ drone | |
| trajectory by an edge-density + spatial-variance scoring heuristic over 10 waypoints × 12 | |
| yaws of candidate frames per scene. **Full gallery:** [`previews/INDEX.md`](previews/INDEX.md). | |
| | | | | | | |
| |---|---|---|---| | |
| |  |  |  |  | | |
| | `hssd-102817053` | `hssd-105515151_173104068` | `hssd-107734119_175999932` | `hssd-107734146_175999971` | | |
| |  |  |  |  | | |
| | `hssd-108736872_177263607` | `hssd-102816729` | `hssd-104348133_171513054` | `hssd-108294870_176710551` | | |
| |  |  |  |  | | |
| | `hssd-108736722_177263382` | `hssd-108294465_176709960` | `hssd-106366323_174226647` | `hssd-108294624_176710203` | | |
| |  |  |  |  | | |
| | `warehouse_v00` | `hospital_v00` | `rivermark_v00` | `office_v00` | | |
| ## License | |
| - **Dataset (this repo):** [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/) | |
| inheriting HSSD's NonCommercial restriction. HSSD attribution preserved per source license. | |
| - **Code, model checkpoints, benchmark:** Apache-2.0 (see linked code repo). | |
| ## Quick start | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| import numpy as np | |
| # Download a single scene | |
| path = snapshot_download( | |
| repo_id="astralhf/yonder", | |
| repo_type="dataset", | |
| allow_patterns="indoor/drone-data/augmented/hssd-102343992/*.npz", | |
| ) | |
| # Load a waypoint | |
| data = np.load(f"{path}/indoor/drone-data/augmented/hssd-102343992/hssd-102343992_wp0000.npz") | |
| left_rgb_yaw0 = data["yaw000_left_rgb"] # shape (480, 640, 3) | |
| forward_depth_yaw0 = data["yaw000_forward_depth"] # shape (480, 640) float16 | |
| lidar = data["lidar360"] # shape (1024, 16) float32 | |
| ``` | |
| ## Reviewer sample | |
| For NeurIPS reviewers and others who want a small smoke-test before downloading | |
| multiple TB, see the companion subset: | |
| **[`astralhf/yonder-sample`](https://huggingface.co/datasets/astralhf/yonder-sample)** | |
| (~500 MB, one HSSD scene, all 12 yaws per waypoint). | |
| ## Repository layout | |
| ``` | |
| indoor/drone-data/augmented/ ← paper's primary subset (4.65M frames) | |
| ├── hssd-102343992/ | |
| │ ├── manifest.json | |
| │ ├── hssd-102343992_wp0000.npz | |
| │ └── ... | |
| └── hssd-*/ (167 scene dirs) | |
| annotations/ ← COCO-format detection labels | |
| └── hssd-*/ | |
| ├── annotations.json (per-scene bbox annotations) | |
| └── object_inventory.json (per-scene object catalog) | |
| indoor/isaac-sim-native/ ← cross-simulator evaluation subset | |
| ├── scenes/ (60 Isaac-rendered indoor scenes) | |
| └── annotations/ | |
| outdoor/ ← sibling resources (not part of the NeurIPS paper) | |
| ├── boreal/ coastal/ desert/ forest/ lunar/ | |
| ├── infinigen/ (procedural Infinigen scenes) | |
| ├── carla-cities/ (8 CARLA towns, full UE4 city geometry) | |
| └── carla-roads/ (8 CARLA towns, drivable surface only) | |
| ``` | |
| Each scene directory under `indoor/drone-data/augmented/` contains: | |
| - `manifest.json` — scene-level metadata (scene_id, sampling parameters, total_waypoints, | |
| total_frames, unique_object_ids). | |
| - `<scene_id>_wp####.npz` — one NPZ file per waypoint, each holding 12 yaws across all sensor | |
| modalities. | |
| ## Cross-simulator evaluation subset (`indoor/isaac-sim-native/`) | |
| Yonder's central thesis — that perception trained on one simulator does not transfer | |
| cleanly to another — requires a different-simulator evaluation set. We provide | |
| **60 Isaac-rendered indoor scenes** (warehouse, hospital, office variants) under | |
| `indoor/isaac-sim-native/scenes/`, with companion annotations under | |
| `indoor/isaac-sim-native/annotations/`. These scenes are used for closed-loop | |
| navigation evaluation in the paper. | |
| ## Sibling resources (not part of the NeurIPS paper) | |
| The repository also hosts outdoor scene assets for separate research not described | |
| in the Yonder paper. These are USD scene geometry (different schema from the indoor | |
| waypoint NPZs): | |
| - `outdoor/{boreal,coastal,desert,forest,lunar}/` — biome-specific scenes with | |
| per-biome `previews/`, `prototypes/`, and `scenes/` directories. | |
| - `outdoor/infinigen/` — procedural scenes generated with [Infinigen](https://infinigen.org/), | |
| 16 scenes spanning canyon, coast, desert, forest, mountain biomes. | |
| - `outdoor/carla-cities/` — full UE4 city geometry from CARLA's 8 towns, | |
| ~14k mesh instances total. Open in Isaac Sim with | |
| `omni.usd.get_context().open_stage("outdoor/carla-cities/Town03/scene.usd")`. | |
| - `outdoor/carla-roads/` — drivable-surface USD only, derived from CARLA OpenDRIVE | |
| (MIT-licensed). 8 towns, plus 380k+ semantic axis-aligned bboxes per town. | |
| These outdoor resources are governed by their respective upstream licenses (CARLA: MIT; | |
| Infinigen: BSD-3-Clause). They are **not** part of the NeurIPS paper's claims and | |
| should be evaluated against their own licenses if used. | |
| ## Splits | |
| Yonder is released without pre-defined train/val/test splits. The paper's experiments | |
| use a held-out set of 10% of waypoints (uniformly sampled across scenes); see | |
| the paper for the exact split file (also released alongside the code). | |
| ## Intended use | |
| ✅ **Recommended:** | |
| - Training drone-perspective perception models (open-vocabulary detection, | |
| monocular depth, semantic segmentation). | |
| - Studying cross-simulator generalization. *Pair offline metrics with closed-loop | |
| evaluation in a different simulator.* The whole point of Yonder is that doing | |
| only the former is misleading. | |
| - Benchmarking long-horizon visual-language navigation, when paired with a | |
| closed-loop evaluator. | |
| ⚠ **Use with caution:** | |
| - End-to-end navigation policy training. Yonder is a perception-training resource; | |
| we do not provide expert trajectories for behavior cloning. | |
| - Any metric reported on Yonder's offline evaluation split alone, without a | |
| closed-loop counterpart, may not reflect deployment performance. | |
| 🚫 **Not for:** | |
| - Commercial use (the entire dataset inherits HSSD's CC-BY-NC restriction). | |
| - Surveillance, biometric identification, or any application of open-vocabulary | |
| detection to identify specific real persons. The simulated scenes contain no | |
| real persons; transfer to person-identification tasks is out of scope and | |
| expressly disallowed. | |
| ## Responsible AI considerations | |
| - **No real persons.** Yonder is rendered entirely from synthetic 3D scenes (HSSD); | |
| no humans are present in any frame. No PII, no biometric data, no faces. | |
| - **Synthetic-only domain.** Performance on Yonder does not transfer to real | |
| imagery without explicit sim-to-real treatment. Anyone deploying perception | |
| trained on Yonder in the real world must perform their own real-domain validation. | |
| - **Geographic / cultural bias.** HSSD scenes are biased toward Western residential | |
| interiors. Models trained on Yonder may underperform on interior styles outside | |
| this distribution. | |
| - **Cross-simulator evaluation is mandatory.** The dataset's primary contribution | |
| is making it easy to discover that fine-tuning gains can be illusory. | |
| Models reported on Yonder should be validated in a different simulator (or | |
| the real world) before claims of improvement are made. | |
| See the accompanying Croissant metadata (`yonder.croissant.json`) for machine-readable | |
| RAI fields. | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{anonymous2026yonder, | |
| title = {Yonder: A 4.65M-Frame Drone Navigation Dataset and the Cross-Simulator Generalization Gap}, | |
| author = {Anonymous Author(s)}, | |
| booktitle = {Advances in Neural Information Processing Systems (Datasets and Benchmarks Track)}, | |
| year = {2026}, | |
| note = {Anonymized for double-blind review.} | |
| } | |
| ``` | |
| ## Authors and contact | |
| Authors and affiliation are anonymized for NeurIPS double-blind review. After | |
| review, the camera-ready version will list authors and contact information here. | |
| ## Changelog | |
| - **2026-05-01** — Initial release: 167 HSSD scenes / 387,527 waypoints / 4.65M frames / | |
| semantic annotations for all 167. HM3D, Replica, and ReplicaCAD subsets removed prior | |
| to release (HM3D and Replica for license incompatibility; ReplicaCAD for lacking | |
| semantic annotations and not being used in any reported experiment). Three Habitat | |
| test scenes (apartment_1, skokloster-castle, van-gogh-room) also excluded for | |
| upstream-license incompatibility. | |