---
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}
}
```