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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+pretty_name: "BLV Object Recognition (Synthetic + Real-World)"
+license: cc-by-4.0
+language:
+  - en
+task_categories:
+  - image-segmentation
+  - object-detection
+task_ids:
+  - semantic-segmentation
+  - instance-segmentation
+size_categories:
+  - 100K<n<1M
+tags:
+  - accessibility
+  - blind-low-vision
+  - synthetic
+  - isaacsim
+  - sim-to-real
+  - urban-navigation
+configs:
+  - config_name: syn
+    data_files:
+      - split: train
+        path: syn/train/data-*.parquet
+  - config_name: real_ours
+    data_files:
+      - split: train
+        path: real_ours/train/data-*.parquet
+      - split: validation
+        path: real_ours/validation/data-*.parquet
+      - split: test
+        path: real_ours/test/data-*.parquet
+  - config_name: real_curated
+    data_files:
+      - split: train
+        path: real_curated/train/data-*.parquet
+dataset_info:
+  - config_name: syn
+    features:
+      - name: image
+        dtype: image
+      - name: mask
+        dtype: image
+      - name: source
+        dtype: string
+      - name: object_class
+        dtype: string
+      - name: class_id
+        dtype: int32
+      - name: environment
+        dtype: string
+      - name: sublocation
+        dtype: string
+      - name: asset
+        dtype: string
+      - name: trajectory
+        dtype: string
+      - name: frame_index
+        dtype: int32
+      - name: bbox
+        sequence:
+          sequence: int32
+      - name: bbox_class_ids
+        sequence: int32
+      - name: occlusion_ratio
+        sequence: float32
+      - name: width
+        dtype: int32
+      - name: height
+        dtype: int32
+  - config_name: real_ours
+    features:
+      - name: image
+        dtype: image
+      - name: mask
+        dtype: image
+      - name: source
+        dtype: string
+      - name: object_class
+        dtype: string
+      - name: class_id
+        dtype: int32
+      - name: location
+        dtype: string
+      - name: width
+        dtype: int32
+      - name: height
+        dtype: int32
+  - config_name: real_curated
+    features:
+      - name: image
+        dtype: image
+      - name: mask
+        dtype: image
+      - name: source
+        dtype: string
+      - name: source_dataset
+        dtype: string
+      - name: split_origin
+        dtype: string
+      - name: width
+        dtype: int32
+      - name: height
+        dtype: int32
+---
+
+> **4 GB stratified preview.** Full dataset: [NavAble/NeurIPS_2026_BLV](https://huggingface.co/datasets/NavAble/NeurIPS_2026_BLV).
+
+# BLV Object Recognition: Synthetic + Real-World
+
+A dataset for training and evaluating object recognition and segmentation
+models on infrastructure relevant to **blind and low-vision (BLV) navigation**
+in urban environments. Three configurations plus a flat tree of 3D assets:
+
+| Config / tree | Splits | Purpose |
+|---------------|--------|---------|
+| `syn`           | `train` | Photorealistic IsaacSim renders for training / pretraining. |
+| `real_ours`     | `train` / `validation` / `test` | Real photographs we captured. **`real_ours/test` is the canonical benchmark eval.** |
+| `real_curated`  | `train` | Curated frames from public HF segmentation datasets (`curation`, `mapillary`), remapped to our class palette. |
+| `synthetic_objects/` (tree) | n/a | 3D asset library: per-asset `.glb` + `.ply` + `.usdz` triples grouped by BLV class. |
+
+![samples](docs/hero.png)
+
+## Quick links
+
+- [Datasheet for Datasets](docs/datasheet.md)
+- [Class index + palette](class_index.json)
+- Croissant metadata is auto-generated by Hugging Face for this repo (look for the *Croissant* button on the dataset page).
+- Paper: NeurIPS 2026 Datasets & Benchmarks (TBD).
+
+## Loading
+
+### With `datasets`
+
+```python
+from datasets import load_dataset
+
+syn_train     = load_dataset("NavAble/NeurIPS_2026_BLV", "syn",          split="train")
+ours_train    = load_dataset("NavAble/NeurIPS_2026_BLV", "real_ours",    split="train")
+ours_val      = load_dataset("NavAble/NeurIPS_2026_BLV", "real_ours",    split="validation")
+ours_test     = load_dataset("NavAble/NeurIPS_2026_BLV", "real_ours",    split="test")  # canonical eval
+curated_train = load_dataset("NavAble/NeurIPS_2026_BLV", "real_curated", split="train")
+
+row = ours_test[0]
+row["image"]   # PIL.Image.Image, RGB
+row["mask"]    # PIL.Image.Image, P-mode (palette) - pixel value == class_id
+```
+
+### Pulling the 3D assets
+
+```python
+from huggingface_hub import snapshot_download
+
+# All 3D assets for a single class:
+snapshot_download(
+    repo_id="NavAble/NeurIPS_2026_BLV", repo_type="dataset",
+    allow_patterns=["synthetic_objects/door_button/**"],
+    local_dir="./assets",
+)
+```
+
+### With PyTorch directly
+
+```python
+from torch.utils.data import Dataset
+from datasets import load_dataset
+import numpy as np
+import torch
+import torchvision.transforms.functional as TF
+
+class BLVSegDataset(Dataset):
+    def __init__(self, config: str, split: str, image_size: int = 512):
+        self.ds = load_dataset("NavAble/NeurIPS_2026_BLV", config, split=split)
+        self.image_size = image_size
+
+    def __len__(self):
+        return len(self.ds)
+
+    def __getitem__(self, idx):
+        row = self.ds[idx]
+        img = TF.resize(row["image"].convert("RGB"), [self.image_size, self.image_size])
+        mask = TF.resize(row["mask"], [self.image_size, self.image_size],
+                         interpolation=TF.InterpolationMode.NEAREST)
+        img_t  = TF.to_tensor(img)
+        mask_t = torch.from_numpy(np.array(mask, dtype=np.int64))
+        return {"image": img_t, "mask": mask_t, "class": row["object_class"]}
+```
+
+## Splits & sizes
+
+| Config         | Split        | Rows |
+|----------------|--------------|------|
+| `syn`          | train        | 452704 |
+| `real_ours`    | train        | 3703 |
+| `real_ours`    | validation   | 396 |
+| `real_ours`    | test         | 1482 |
+| `real_curated` | train        | 36466 |
+
+3D asset library (`synthetic_objects/`): 500 GLB+PLY+USDZ triples across 9 classes.
+
+## Class taxonomy
+
+| ID | Class | Synthetic | Real (Ours) |
+|----|-------|-----------|-------------|
+| 1 | `aps_button` | yes | yes |
+| 2 | `bus_stop` | yes | yes |
+| 3 | `bus_stop_sign` | yes | yes |
+| 4 | `crosswalk` | yes | yes |
+| 5 | `door_button` | yes | yes |
+| 6 | `elevator` | yes | yes |
+| 7 | `elevator_button` | yes | yes |
+| 8 | `escalator` | yes | yes |
+| 9 | `handrail` | yes | yes |
+| 10 | `pedestrian_signal` | yes | yes |
+| 11 | `turnstile` | yes | no |
+
+The synthetic-only class `turnstile` has no real-world examples in this release;
+report real-world metrics over the 10 shared classes.
+
+## Per-class row counts
+
+| Class | syn/train | real_ours/train | real_ours/val | real_ours/test | real_curated/train |
+|---|---|---|---|---|---|
+| `aps_button` | 62855 | 206 | 23 | 66 | 0 |
+| `bus_stop` | 60789 | 205 | 23 | 62 | 0 |
+| `bus_stop_sign` | 60480 | 140 | 16 | 54 | 0 |
+| `crosswalk` | 54360 | 9 | 1 | 3 | 27786 |
+| `door_button` | 45360 | 1327 | 148 | 622 | 0 |
+| `elevator` | 23760 | 1065 | 119 | 479 | 15 |
+| `elevator_button` | 23350 | 378 | 23 | 86 | 4401 |
+| `escalator` | 7062 | 135 | 15 | 40 | 1296 |
+| `handrail` | 44468 | 21 | 3 | 8 | 1197 |
+| `pedestrian_signal` | 45210 | 217 | 25 | 62 | 6650 |
+| `turnstile` | 25010 | 0 | 0 | 0 | 0 |
+
+## Mask encoding
+
+Each `mask` is a single-channel PNG (PIL `mode="P"`) with an embedded palette.
+Pixel value `i` corresponds to the `i`-th entry in `class_index.json`:
+
+| Pixel | Class | Palette RGB |
+|-------|-------|-------------|
+| 0 | `BACKGROUND` | (0, 0, 0) |
+| 1 | `aps_button` | (220, 20, 60) |
+| 2 | `bus_stop` | (255, 140, 0) |
+| 3 | `bus_stop_sign` | (255, 215, 0) |
+| 4 | `crosswalk` | (50, 205, 50) |
+| 5 | `door_button` | (0, 191, 255) |
+| 6 | `elevator` | (138, 43, 226) |
+| 7 | `elevator_button` | (255, 105, 180) |
+| 8 | `escalator` | (0, 128, 128) |
+| 9 | `handrail` | (165, 42, 42) |
+| 10 | `pedestrian_signal` | (75, 0, 130) |
+| 11 | `turnstile` | (255, 20, 147) |
+
+Convert to a numeric label map with `np.array(row["mask"])`.
+
+
+## Preprocessing
+
+Produced by `scripts/build_hf_dataset.py`. Synthetic RGB PNGs are hardlinked
+unchanged from the source tree; the IsaacSim RGBA-encoded semantic masks are
+converted into single-channel palettized PNGs against a global class index;
+synthetic 2D bounding-box `.npy` files are flattened into JSONL columns; the
+real-world COCO polygon annotations are rasterized to the same palettized PNG
+format using `pycocotools`.
+
+## Known limitations
+
+- **Resolution mismatch.** Synthetic frames are 1280×720; real-world frames
+  are 640×360. Models that resize to a common input shape are unaffected.
+- **Class imbalance in real-world data.** Some classes have few real-world
+  examples (e.g. `crosswalk`, `handrail`). Report per-class mIoU alongside any
+  aggregate.
+- **`turnstile` is synthetic-only.** Evaluate over the 10 shared classes for
+  real-world metrics.
+- **Sim-to-real gap.** Synthetic textures and lighting may not match
+  real-world distributions perfectly.
+
+## Ethical considerations
+
+- The synthetic data contains no personally identifiable information.
+- Real-world captures were collected in public spaces (All faces have been blurred.); the dataset is intended
+  for accessibility research.
+- The class taxonomy targets infrastructure relevant to blind/low-vision
+  navigation; models trained on this dataset should not be deployed in
+  safety-critical settings without additional validation.
+
+## License
+
+Released under **CC BY 4.0**.
+
+## Citation
+
+```bibtex
+@inproceedings{navable2026,
+  title     = {NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation},
+  author    = {Anonymized Authors},
+  booktitle = {NeurIPS 2026 Datasets and Benchmarks Track},
+  year      = {2026}
+}
+```

class_index.json

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+{
+  "BACKGROUND": {
+    "id": 0,
+    "color": [
+      0,
+      0,
+      0
+    ]
+  },
+  "aps_button": {
+    "id": 1,
+    "color": [
+      220,
+      20,
+      60
+    ]
+  },
+  "bus_stop": {
+    "id": 2,
+    "color": [
+      255,
+      140,
+      0
+    ]
+  },
+  "bus_stop_sign": {
+    "id": 3,
+    "color": [
+      255,
+      215,
+      0
+    ]
+  },
+  "crosswalk": {
+    "id": 4,
+    "color": [
+      50,
+      205,
+      50
+    ]
+  },
+  "door_button": {
+    "id": 5,
+    "color": [
+      0,
+      191,
+      255
+    ]
+  },
+  "elevator": {
+    "id": 6,
+    "color": [
+      138,
+      43,
+      226
+    ]
+  },
+  "elevator_button": {
+    "id": 7,
+    "color": [
+      255,
+      105,
+      180
+    ]
+  },
+  "escalator": {
+    "id": 8,
+    "color": [
+      0,
+      128,
+      128
+    ]
+  },
+  "handrail": {
+    "id": 9,
+    "color": [
+      165,
+      42,
+      42
+    ]
+  },
+  "pedestrian_signal": {
+    "id": 10,
+    "color": [
+      75,
+      0,
+      130
+    ]
+  },
+  "turnstile": {
+    "id": 11,
+    "color": [
+      255,
+      20,
+      147
+    ]
+  }
+}

docs/datasheet.md

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+# Datasheet: BLV Object Recognition (Synthetic + Real-World)
+
+Following the structure of *Datasheets for Datasets* (Gebru et al., 2018).
+
+## Motivation
+
+**For what purpose was the dataset created?**
+To enable training and evaluation of computer-vision models for blind and
+low-vision (BLV) navigation aids. The dataset focuses on infrastructure
+objects that BLV travelers must perceive and interact with (signals, doors,
+escalators, handrails, etc.) and pairs photorealistic synthetic data for
+training with real-world photographs (split into train / val / test) for
+evaluation.
+
+**Who funded the creation of the dataset?**
+DARoS Lab.
+
+## Composition
+
+**What do instances represent?**
+Each instance is a single image with a paired pixel-level segmentation mask.
+Synthetic instances additionally carry 2D bounding boxes per object.
+
+**How many instances are there?**
+- `syn/train`: 452704
+- `real_ours/train`: 3703
+- `real_ours/validation`: 396
+- `real_ours/test`: 1482
+- `real_curated/train`: 36466
+- `synthetic_objects/` (3D assets): 500 across 9 classes
+
+**Does the dataset contain all possible instances?**
+No. The synthetic data is a finite sample drawn from a parameterized
+generation pipeline; the real-world data is a finite collection of
+photographs.
+
+**Is there any missing information?**
+The synthetic-only class `turnstile` has no real-world examples in this
+release.
+
+**Are there errors, sources of noise, or redundancies?**
+- Synthetic masks are produced by IsaacSim's Replicator and may contain edge
+  artifacts at sub-pixel object boundaries.
+- Real-world polygon annotations were authored manually and may have small
+  boundary errors.
+
+## Collection process
+
+**Synthetic.** Generated in NVIDIA IsaacSim using Replicator. Each trajectory
+samples an asset, environment, and lighting condition, and records
+RGB + semantic mask + 2D bounding box per frame.
+
+**Real-world.** Photographs captured by data collectors at distinct physical
+locations covering the 10 shared object classes; annotated with COCO-format
+polygon segmentations.
+
+## Preprocessing / cleaning / labeling
+
+See `README.md` and `scripts/build_hf_dataset.py`. The on-disk layout
+re-encodes synthetic RGBA-coded masks into a single global palettized format
+and rasterizes real-world COCO polygons into the same format. Source RGB PNGs
+are not re-encoded.
+
+## Uses
+
+**Has the dataset been used for any tasks already?**
+The dataset will accompany a paper at the NeurIPS 2026 Datasets & Benchmarks
+track (submission pending).
+
+**What other tasks could the dataset be used for?**
+Sim-to-real transfer studies, robustness analysis under lighting conditions,
+multi-task learning combining detection and segmentation.
+
+**Are there tasks for which the dataset should not be used?**
+The dataset must not be used for surveillance or identification of
+individuals. The synthetic data does not represent real people; the
+real-world data was collected in public spaces and is intended only for
+accessibility research.
+
+## Distribution
+
+The dataset is hosted on Hugging Face at `NavAble/NeurIPS_2026_BLV` and
+licensed under CC BY 4.0.

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