Datasets:
Tasks:
Image-to-3D
Modalities:
Tabular
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 3,725 Bytes
150d753 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | ---
license: apache-2.0
task_categories:
- image-to-3d
language:
- en
tags:
- depth-estimation
- layered-depth
- stratified-sampling
- computer-vision
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: metadata/sample_buckets.parquet
---
# layerdepth-stratified
**Stratified train metadata for [princeton-vl/LayeredDepth-Syn](https://huggingface.co/datasets/princeton-vl/LayeredDepth-Syn).**
This dataset does **not** duplicate LayeredDepth images (~1.9 TB). It publishes:
1. Per-scene **bucket assignments** (compressed layer count 1–4)
2. A **round-robin batch mix manifest** for balanced multilayer training
3. **Preprocessing code** aligned with the original LayeredDepth / SeeGroup pipeline
Use it to train models that avoid fake-layer collapse by balancing layer-count buckets each epoch.
## Base dataset
| Item | Value |
|------|-------|
| Images & depth PNGs | [princeton-vl/LayeredDepth-Syn](https://huggingface.co/datasets/princeton-vl/LayeredDepth-Syn) |
| Train scenes | 14,800 |
| Layers per scene | 4 depth maps (IDs 1, 3, 5, 7) |
| This repo | Metadata + sampling code only |
## Files
| Path | Description |
|------|-------------|
| `metadata/sample_buckets.parquet` | One row per train scene: `row_index`, `bucket`, layer stats |
| `metadata/bucket_manifest.json` | Bucket → row_index lists + default `batch_mix` |
| `metadata/summary.json` | Build provenance and histogram |
| `layerdepth_stratified/preprocess.py` | LayeredDepth depth collapse + RGB/depth decoding |
| `layerdepth_stratified/stratified_sampling.py` | Epoch order + DDP rank split |
| `layerdepth_stratified/dataset_loader.py` | High-level iterator API |
## Quick start
```python
from datasets import load_dataset
from layerdepth_stratified import iter_from_hub_metadata
# 1) Load stratified metadata from this repo
meta = load_dataset("YOUR_USERNAME/layerdepth-stratified", split="train")
print(meta[0]) # row_index, bucket, scene_max_compressed, ...
# 2) Iterate preprocessed samples in stratified epoch order
for sample in iter_from_hub_metadata(
"YOUR_USERNAME/layerdepth-stratified",
cache_dir="/path/to/hf/cache",
seed=42,
epoch=1,
):
image = sample["image"] # float32 HWC, [0, 1]
depth = sample["depth"] # float32 HWD, meters, LayeredDepth convention
valid_mask = sample["valid_mask"]
break
```
## Preprocessing (matches original LayeredDepth)
1. Decode RGB PNG → float32 `[0, 1]`
2. Decode depth PNG → meters (`/1000`, clip invalid/`>80m`)
3. **Layer collapse**: invalid shallow pixels inherit deeper valid depth (standard LayeredDepth convention)
4. Optional layer subset via `selected_layer_ids`
See `layerdepth_stratified/preprocess.py` for the reference implementation.
## Stratified sampling
Each train scene is assigned to bucket **1–4** by **compressed layer count** (ray-sort + gap events).
Default per-batch mix (`batch_mix`):
```json
{"1": 0.25, "2": 0.25, "3": 0.25, "4": 0.25}
```
Each epoch:
1. Shuffle within each bucket
2. Round-robin across buckets using `batch_mix`
3. Map indices → rows in `princeton-vl/LayeredDepth-Syn` train split
## Rebuild manifest
```bash
python -m layerdepth_stratified.build_manifest \
--cache-dir /path/to/hf/datasets \
--output-dir artifacts/layereddepth_stratified
```
## Citation
If you use this stratified metadata, please cite the original LayeredDepth paper/dataset and note that bucket assignments were produced with the SeeGroup stratified sampling pipeline.
## See also
- [LayeredDepth-Syn](https://huggingface.co/datasets/princeton-vl/LayeredDepth-Syn)
- SeeGroup `docs/LAYEREDDEPTH_STRATIFIED_SAMPLING.md`
|