docs: polish Pose convention section wording

README.md

@@ -1,215 +1,215 @@
----
-pretty_name: ZereData Bin Picking Dataset v1.1
-license: cc-by-4.0
-task_categories:
-  - object-detection
-  - image-segmentation
-size_categories:
-  - 10K<n<100K
-tags:
-  - synthetic-data
-  - bin-picking
-  - robotics
-  - 6d-pose
-  - pose-estimation
-  - depth-estimation
-  - instance-segmentation
-  - warehouse
-  - coco
-  - yolo
-  - bop
-  - pbr
-  - computer-vision
-language:
-  - en
----
-
-# ZereData Bin Picking Dataset v1.1
-
-![license](https://img.shields.io/badge/license-CC--BY--4.0-blue) ![scenes](https://img.shields.io/badge/scenes-10,000-green) ![size](https://img.shields.io/badge/size-14.8GB-orange)
-
-Synthetic training data for robotic bin picking — RGB, depth, instance masks, 6D pose, 2D bounding boxes, and per-instance visibility, in BOP/COCO/YOLO formats.
-
-![preview](preview/0001_scene_0016.png) ![preview](preview/0002_scene_0059.png) ![preview](preview/0003_scene_0001.png) ![preview](preview/0004_scene_0004.png)
-
-## Overview
-
-Generated via physically-based ray tracing in Blender Cycles, this dataset delivers dense, photorealistic scenes of cluttered bins at warehouse scale. Each scene includes RGB, 32-bit depth, instance segmentation, camera intrinsics/extrinsics, and per-instance 6D pose with visibility ratios.
-
-The dataset's value is simple: synthetic renders give perfect ground truth annotations impossible to obtain from real cameras, at a scale and cost real-world collection cannot match. Use it to train 6D pose estimators, bin-picking grasp predictors, and warehouse perception systems — then validate sim-to-real transfer on smaller real-world test sets.
-
-## Dataset Statistics
-
-| Metric | Value |
-|--------|-------|
-| Total scenes | 10,000 |
-| Train split | 8,000 |
-| Val split | 2,000 |
-| Resolution | 1280x720 |
-| Object instances | 296,603 |
-| Object categories | 4 |
-| Modalities | 6 (RGB, depth, mask, pose, bboxes, visibility) |
-| Total size on disk | 14.8 GB |
-
-## Modalities
-
-- **RGB** — 1280×720 PNG per scene. The primary input for detection, segmentation, and pose models.
-- **Depth** — 32-bit EXR in metres. Train depth-conditioned pose models or use as a second-channel input.
-- **Instance mask** — colour-coded PNG per scene, one colour per object instance. Drives instance segmentation and occlusion reasoning.
-- **6D pose** — per-instance rotation and translation in camera frame (BOP `cam_R_m2c`, `cam_t_m2c`). Supervises pose regression heads.
-- **2D bounding boxes** — derived from masks, included in COCO and YOLO formats.
-- **Visibility ratio** — BOP `visib_fract` per instance; lets you weight the training loss by occlusion severity.
-
-## Formats
-
-### BOP (primary)
-Canonical BOP directory layout under `data/train/` and `data/val/`. Each scene folder contains `scene_camera.json` (`cam_K`, `depth_scale`), `scene_gt.json` (per-object `cam_R_m2c`, `cam_t_m2c`, `obj_id`), and `scene_gt_info.json` (`bbox_obj`, `bbox_visib`, `visib_fract`). Load with the BOP toolkit. Object IDs are **ZereData-specific, not BOP canonical** — see Limitations.
-
-### COCO
-Merged `annotations/coco_train.json` and `annotations/coco_val.json` with `images`, `annotations` (bboxes + masks), and `categories`. Loads cleanly with pycocotools:
-
-```python
-from pycocotools.coco import COCO
-coco = COCO('annotations/coco_train.json')
-```
-
-### YOLO
-Per-image `.txt` label files under `annotations/yolo_train/` and `yolo_val/`, with normalized `class_id cx cy w h` entries. Class IDs are consistent across both splits; see `annotations/yolo_classes.txt` and `annotations/yolo_data.yaml`.
-
-## Data Format
-
-This dataset is packaged as per-format zip archives, mirroring the [bop-benchmark](https://huggingface.co/bop-benchmark) HF layout convention (one zip per logical split) adapted for multi-format shipping. Loose files — README, LICENSE, CITATION, metadata.json, preview images — remain at the repository root so the HF dataset page renders a preview.
-
-| Archive | Contents | On-extract layout |
-|---|---|---|
-| `bin_picking_train_bop.zip` | BOP-format train split (rgb/depth/mask + `scene_camera.json` / `scene_gt.json` / `scene_gt_info.json` per scene) | `data/train/{000000..007999}/...` |
-| `bin_picking_val_bop.zip` | BOP-format val split | `data/val/{000000..001999}/...` |
-| `bin_picking_coco.zip` | `coco_train.json`, `coco_val.json` (merged, BOP obj IDs remapped to COCO categories) | `annotations/coco_*.json` |
-| `bin_picking_yolo.zip` | YOLO labels per split + `yolo_classes.txt` + `yolo_data.yaml` | `annotations/yolo_{train,val}/*.txt`, `annotations/yolo_*.{txt,yaml}` |
-| `bin_picking_native.zip` | Per-scene native annotations (full pre-export ZereData scene graph) | `annotations/scene_NNNN.json` |
-| `bin_picking_models.zip` | 27 GLB object models | `models/*.glb` |
-
-### Download and extract only what you need
-
-```python
-from huggingface_hub import hf_hub_download
-import zipfile
-
-REPO = 'zeredata/bin-picking-v1'
-# BOP train split
-p = hf_hub_download(repo_id=REPO, filename='bin_picking_train_bop.zip', repo_type='dataset')
-with zipfile.ZipFile(p) as z:
-    z.extractall('./zd_bp')   # rehydrates ./zd_bp/data/train/...
-```
-
-Or the whole dataset in one shot:
-
-```bash
-huggingface-cli download --repo-type dataset zeredata/bin-picking-v1 --local-dir ./zd_bp
-cd ./zd_bp && for z in bin_picking_*.zip; do unzip -q "$z"; done
-```
-
-All zip extractions share the same root-relative layout, so unzipping all six archives into one directory rehydrates the canonical flat tree.
-
-## Loading the Dataset
-
-These snippets assume you have already extracted the relevant zip(s) into a working directory (see **Data Format** above). Paths are relative to that root.
-
-### PyTorch Dataset over BOP structure
-```python
-from pathlib import Path
-from torch.utils.data import Dataset
-from PIL import Image
-import json
-
-class BopBinPicking(Dataset):
-    def __init__(self, root, split='train'):
-        # root must contain data/<split>/... (extract bin_picking_<split>_bop.zip there first)
-        self.scene_dirs = sorted((Path(root) / 'data' / split).iterdir())
-    def __len__(self):
-        return len(self.scene_dirs)
-    def __getitem__(self, idx):
-        sd = self.scene_dirs[idx]
-        rgb = Image.open(sd / 'rgb' / '000000.png')
-        gt = json.loads((sd / 'scene_gt.json').read_text())
-        cam = json.loads((sd / 'scene_camera.json').read_text())
-        return rgb, gt, cam
-```
-
-### COCO via pycocotools
-```python
-# After extracting bin_picking_coco.zip:
-from pycocotools.coco import COCO
-coco = COCO('annotations/coco_train.json')
-img_ids = coco.getImgIds()
-for ann in coco.loadAnns(coco.getAnnIds(imgIds=img_ids[0])):
-    print(ann['bbox'], ann['category_id'])
-```
-
-_A `datasets.load_dataset()` loader is planned for v1.1._
-
-## Intended Use
-
-Training 6D pose estimation models, bin-picking grasp models, and warehouse robotics perception systems. Synthetic data for sim-to-real transfer research.
-
-## Pose convention
-
-This release (v1.0 and v1.1) ships 6D poses in **OpenGL/Blender camera convention** (camera looks down `-Z`, in-front objects have `cam_t_m2c.z < 0`) rather than the BOP-standard **OpenCV convention** (camera looks down `+Z`, in-front objects have `cam_t_m2c.z > 0`). The two differ by the basis change `diag(1, -1, -1)` applied to the camera frame.
-
-**Anyone scoring against bop_toolkit_lib, MegaPose, FoundationPose, CosyPose, or any OpenCV-convention model must apply `diag(1, -1, -1)` to the GT `cam_R_m2c` and `cam_t_m2c` from `scene_gt.json` before evaluation**, otherwise pose errors blow up to the order of the object diameter (~150 mm) and any AR comparison is meaningless.
-
-ZereData's evaluation harness exposes this via the `--legacy-gl-convention` flag on `eval.scorer.BopScorer`, `eval.adapter.gt_as_predictions`, and the eval CLIs (`gt_sanity`, `run_megapose_eval`, `rescore`, `_perturb_probe`, `rescore_h1`). Pass it when scoring v1.0 or v1.1; omit it for v1.2 onward, which ships in OpenCV convention at the producer (see [zrdt-269](https://github.com/umitkavala/zere-synth/commit/018b959) for the producer-side fix).
-
-This deviation was identified post-publication. v1.0 and v1.1 remain available unchanged on HuggingFace for reproducibility; **v1.2 supersedes them for new integrations** and ships BOP-spec compliant out of the box. The legacy flag is supported indefinitely so downstream code that already ingests v1.0/v1.1 keeps working.
-
-RGB, depth, masks, COCO 2D boxes, and YOLO labels are **unaffected** — only the 6D pose serialisation deviates from the BOP spec.
-
-## Limitations and Known Issues
-
-- **BOP pose convention deviation.** Object pose extrinsics in `scene_gt.json` ship in OpenGL convention, not BOP-spec OpenCV. See the **Pose convention** section above for the basis change and the `--legacy-gl-convention` flag. v1.2 supersedes this with a producer-side fix.
-- **Warehouse-specific lighting.** The three lighting profiles model warehouse conditions and may not transfer directly to outdoor, medical, or agricultural domains:
-    - `bin_picking_overhead` — bright fluorescent overhead panels, typical of distribution-center shelving aisles.
-    - `bin_picking_mixed` — mixed overhead + rim lighting with warmer colour temperature, mimicking older facilities with partial skylights.
-    - `studio` — three-point studio lighting setup shared across ZereData scenarios; in bin-picking scenes, produces low-light conditions with deep shadows.
-  Each scene's `variety.lighting_profile` annotation tag records which profile was used.
-- **Procedural materials.** Material variation uses procedural textures, not photoscanned assets. High-frequency surface detail may look synthetic under close inspection.
-- **Geometric occlusion only.** No category-level occlusion modelling — occlusion is derived from geometry alone.
-- **Simulated camera intrinsics.** The intrinsic matrix is synthetic, not drawn from real sensor calibration.
-
-## Evaluation
-
-Benchmark evaluation on LM-O is forthcoming; see [ZereData](https://zeredata.com) for updates.
-
-## Comparison to Related Datasets
-
-HOPE, T-LESS, and YCB-Video are excellent real-world datasets with limited scale and fixed object sets. This dataset is synthetic-only, scales without bound, and supports customer-specific object libraries. Treat the two as complementary: real data for evaluation, synthetic data for training.
-
-
-## Custom Datasets
-
-This release is a research dataset. The categories (bottle, box, can, pouch), SKU shapes, and bin geometry are intentionally generic — useful for benchmarking, pretraining, and sanity-checking a 6D pose pipeline before you invest in real-world data collection.
-
-For production use, ZereData generates the same kind of dataset matched to your warehouse's actual SKUs and bin geometry. Customer-specific datasets ingest CAD files or reference photos, render at the same scale and quality as this release, and ship in days. Pricing is per-dataset, with design-partner terms for early customers.
-
-If you're training bin-picking models for a specific picking environment, email **engineering@zeredata.com** — design partners welcome.
-
-## Citation
-
-```bibtex
-@dataset{zeredata_binpicking_2026,
-  author = {Umit Kavala},
-  title = {ZereData Bin Picking Dataset v1.1},
-  year = {2026},
-  publisher = {HuggingFace},
-  url = {https://huggingface.co/datasets/zeredata/bin-picking}
-}
-```
-
-## License
-
-Released under [CC BY 4.0](LICENSE). Attribution required. Commercial use permitted.
-
-## Contact and Links
-
-- Website: [https://zeredata.com](https://zeredata.com)
-- Contact: [engineering@zeredata.com](mailto:engineering@zeredata.com)
+---
+pretty_name: ZereData Bin Picking Dataset v1.1
+license: cc-by-4.0
+task_categories:
+  - object-detection
+  - image-segmentation
+size_categories:
+  - 10K<n<100K
+tags:
+  - synthetic-data
+  - bin-picking
+  - robotics
+  - 6d-pose
+  - pose-estimation
+  - depth-estimation
+  - instance-segmentation
+  - warehouse
+  - coco
+  - yolo
+  - bop
+  - pbr
+  - computer-vision
+language:
+  - en
+---
+
+# ZereData Bin Picking Dataset v1.1
+
+![license](https://img.shields.io/badge/license-CC--BY--4.0-blue) ![scenes](https://img.shields.io/badge/scenes-10,000-green) ![size](https://img.shields.io/badge/size-14.8GB-orange)
+
+Synthetic training data for robotic bin picking — RGB, depth, instance masks, 6D pose, 2D bounding boxes, and per-instance visibility, in BOP/COCO/YOLO formats.
+
+![preview](preview/0001_scene_0016.png) ![preview](preview/0002_scene_0059.png) ![preview](preview/0003_scene_0001.png) ![preview](preview/0004_scene_0004.png)
+
+## Overview
+
+Generated via physically-based ray tracing in Blender Cycles, this dataset delivers dense, photorealistic scenes of cluttered bins at warehouse scale. Each scene includes RGB, 32-bit depth, instance segmentation, camera intrinsics/extrinsics, and per-instance 6D pose with visibility ratios.
+
+The dataset's value is simple: synthetic renders give perfect ground truth annotations impossible to obtain from real cameras, at a scale and cost real-world collection cannot match. Use it to train 6D pose estimators, bin-picking grasp predictors, and warehouse perception systems — then validate sim-to-real transfer on smaller real-world test sets.
+
+## Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total scenes | 10,000 |
+| Train split | 8,000 |
+| Val split | 2,000 |
+| Resolution | 1280x720 |
+| Object instances | 296,603 |
+| Object categories | 4 |
+| Modalities | 6 (RGB, depth, mask, pose, bboxes, visibility) |
+| Total size on disk | 14.8 GB |
+
+## Modalities
+
+- **RGB** — 1280×720 PNG per scene. The primary input for detection, segmentation, and pose models.
+- **Depth** — 32-bit EXR in metres. Train depth-conditioned pose models or use as a second-channel input.
+- **Instance mask** — colour-coded PNG per scene, one colour per object instance. Drives instance segmentation and occlusion reasoning.
+- **6D pose** — per-instance rotation and translation in camera frame (BOP `cam_R_m2c`, `cam_t_m2c`). Supervises pose regression heads.
+- **2D bounding boxes** — derived from masks, included in COCO and YOLO formats.
+- **Visibility ratio** — BOP `visib_fract` per instance; lets you weight the training loss by occlusion severity.
+
+## Formats
+
+### BOP (primary)
+Canonical BOP directory layout under `data/train/` and `data/val/`. Each scene folder contains `scene_camera.json` (`cam_K`, `depth_scale`), `scene_gt.json` (per-object `cam_R_m2c`, `cam_t_m2c`, `obj_id`), and `scene_gt_info.json` (`bbox_obj`, `bbox_visib`, `visib_fract`). Load with the BOP toolkit. Object IDs are **ZereData-specific, not BOP canonical** — see Limitations.
+
+### COCO
+Merged `annotations/coco_train.json` and `annotations/coco_val.json` with `images`, `annotations` (bboxes + masks), and `categories`. Loads cleanly with pycocotools:
+
+```python
+from pycocotools.coco import COCO
+coco = COCO('annotations/coco_train.json')
+```
+
+### YOLO
+Per-image `.txt` label files under `annotations/yolo_train/` and `yolo_val/`, with normalized `class_id cx cy w h` entries. Class IDs are consistent across both splits; see `annotations/yolo_classes.txt` and `annotations/yolo_data.yaml`.
+
+## Data Format
+
+This dataset is packaged as per-format zip archives, mirroring the [bop-benchmark](https://huggingface.co/bop-benchmark) HF layout convention (one zip per logical split) adapted for multi-format shipping. Loose files — README, LICENSE, CITATION, metadata.json, preview images — remain at the repository root so the HF dataset page renders a preview.
+
+| Archive | Contents | On-extract layout |
+|---|---|---|
+| `bin_picking_train_bop.zip` | BOP-format train split (rgb/depth/mask + `scene_camera.json` / `scene_gt.json` / `scene_gt_info.json` per scene) | `data/train/{000000..007999}/...` |
+| `bin_picking_val_bop.zip` | BOP-format val split | `data/val/{000000..001999}/...` |
+| `bin_picking_coco.zip` | `coco_train.json`, `coco_val.json` (merged, BOP obj IDs remapped to COCO categories) | `annotations/coco_*.json` |
+| `bin_picking_yolo.zip` | YOLO labels per split + `yolo_classes.txt` + `yolo_data.yaml` | `annotations/yolo_{train,val}/*.txt`, `annotations/yolo_*.{txt,yaml}` |
+| `bin_picking_native.zip` | Per-scene native annotations (full pre-export ZereData scene graph) | `annotations/scene_NNNN.json` |
+| `bin_picking_models.zip` | 27 GLB object models | `models/*.glb` |
+
+### Download and extract only what you need
+
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+
+REPO = 'zeredata/bin-picking-v1'
+# BOP train split
+p = hf_hub_download(repo_id=REPO, filename='bin_picking_train_bop.zip', repo_type='dataset')
+with zipfile.ZipFile(p) as z:
+    z.extractall('./zd_bp')   # rehydrates ./zd_bp/data/train/...
+```
+
+Or the whole dataset in one shot:
+
+```bash
+huggingface-cli download --repo-type dataset zeredata/bin-picking-v1 --local-dir ./zd_bp
+cd ./zd_bp && for z in bin_picking_*.zip; do unzip -q "$z"; done
+```
+
+All zip extractions share the same root-relative layout, so unzipping all six archives into one directory rehydrates the canonical flat tree.
+
+## Loading the Dataset
+
+These snippets assume you have already extracted the relevant zip(s) into a working directory (see **Data Format** above). Paths are relative to that root.
+
+### PyTorch Dataset over BOP structure
+```python
+from pathlib import Path
+from torch.utils.data import Dataset
+from PIL import Image
+import json
+
+class BopBinPicking(Dataset):
+    def __init__(self, root, split='train'):
+        # root must contain data/<split>/... (extract bin_picking_<split>_bop.zip there first)
+        self.scene_dirs = sorted((Path(root) / 'data' / split).iterdir())
+    def __len__(self):
+        return len(self.scene_dirs)
+    def __getitem__(self, idx):
+        sd = self.scene_dirs[idx]
+        rgb = Image.open(sd / 'rgb' / '000000.png')
+        gt = json.loads((sd / 'scene_gt.json').read_text())
+        cam = json.loads((sd / 'scene_camera.json').read_text())
+        return rgb, gt, cam
+```
+
+### COCO via pycocotools
+```python
+# After extracting bin_picking_coco.zip:
+from pycocotools.coco import COCO
+coco = COCO('annotations/coco_train.json')
+img_ids = coco.getImgIds()
+for ann in coco.loadAnns(coco.getAnnIds(imgIds=img_ids[0])):
+    print(ann['bbox'], ann['category_id'])
+```
+
+_A `datasets.load_dataset()` loader is planned for v1.1._
+
+## Intended Use
+
+Training 6D pose estimation models, bin-picking grasp models, and warehouse robotics perception systems. Synthetic data for sim-to-real transfer research.
+
+## Pose convention
+
+This release (v1.0 and v1.1) ships 6D poses in **OpenGL/Blender camera convention** (camera looks down `-Z`, in-front objects have `cam_t_m2c.z < 0`) rather than the BOP-standard **OpenCV convention** (camera looks down `+Z`, in-front objects have `cam_t_m2c.z > 0`). The two differ by the basis change `diag(1, -1, -1)` applied to the camera frame.
+
+**Anyone scoring against bop_toolkit_lib, MegaPose, FoundationPose, CosyPose, or any OpenCV-convention model must apply `diag(1, -1, -1)` to the GT `cam_R_m2c` and `cam_t_m2c` from `scene_gt.json` before evaluation**, otherwise pose errors blow up to the order of the object diameter (~150 mm) and any AR comparison is meaningless.
+
+ZereData's evaluation harness exposes this via the `--legacy-gl-convention` flag on `eval.scorer.BopScorer`, `eval.adapter.gt_as_predictions`, and the eval CLIs (`gt_sanity`, `run_megapose_eval`, `rescore`, `_perturb_probe`, `rescore_h1`). Pass it when scoring v1.0 or v1.1; omit it for v1.2 onward, which ships in OpenCV convention at the producer (see zrdt-269 (commit `018b959`) for the producer-side fix).
+
+This deviation was identified post-publication. v1.0 and v1.1 remain available unchanged on HuggingFace for reproducibility; **v1.2 supersedes them for new integrations** and ships BOP-spec compliant out of the box. The legacy flag is supported indefinitely so downstream code that already ingests v1.0/v1.1 keeps working.
+
+RGB, depth, masks, COCO 2D boxes, and YOLO labels are **unaffected** — only the 6D pose serialisation deviates from the BOP spec.
+
+## Limitations and Known Issues
+
+- **BOP pose convention deviation.** Object pose extrinsics in `scene_gt.json` ship in OpenGL convention, not BOP-spec OpenCV. See the **Pose convention** section above for the basis change and the `--legacy-gl-convention` flag. v1.2 supersedes this with a producer-side fix.
+- **Warehouse-specific lighting.** The three lighting profiles model warehouse conditions and may not transfer directly to outdoor, medical, or agricultural domains:
+    - `bin_picking_overhead` — bright fluorescent overhead panels, typical of distribution-center shelving aisles.
+    - `bin_picking_mixed` — mixed overhead + rim lighting with warmer colour temperature, mimicking older facilities with partial skylights.
+    - `studio` — three-point studio lighting setup shared across ZereData scenarios; in bin-picking scenes, produces low-light conditions with deep shadows.
+  Each scene's `variety.lighting_profile` annotation tag records which profile was used.
+- **Procedural materials.** Material variation uses procedural textures, not photoscanned assets. High-frequency surface detail may look synthetic under close inspection.
+- **Geometric occlusion only.** No category-level occlusion modelling — occlusion is derived from geometry alone.
+- **Simulated camera intrinsics.** The intrinsic matrix is synthetic, not drawn from real sensor calibration.
+
+## Evaluation
+
+Benchmark evaluation on LM-O is forthcoming; see [ZereData](https://zeredata.com) for updates.
+
+## Comparison to Related Datasets
+
+HOPE, T-LESS, and YCB-Video are excellent real-world datasets with limited scale and fixed object sets. This dataset is synthetic-only, scales without bound, and supports customer-specific object libraries. Treat the two as complementary: real data for evaluation, synthetic data for training.
+
+
+## Custom Datasets
+
+This release is a research dataset. The categories (bottle, box, can, pouch), SKU shapes, and bin geometry are intentionally generic — useful for benchmarking, pretraining, and sanity-checking a 6D pose pipeline before you invest in real-world data collection.
+
+For production use, ZereData generates the same kind of dataset matched to your warehouse's actual SKUs and bin geometry. Customer-specific datasets ingest CAD files or reference photos, render at the same scale and quality as this release, and ship in days. Pricing is per-dataset, with design-partner terms for early customers.
+
+If you're training bin-picking models for a specific picking environment, email **engineering@zeredata.com** — design partners welcome.
+
+## Citation
+
+```bibtex
+@dataset{zeredata_binpicking_2026,
+  author = {Umit Kavala},
+  title = {ZereData Bin Picking Dataset v1.1},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/datasets/zeredata/bin-picking}
+}
+```
+
+## License
+
+Released under [CC BY 4.0](LICENSE). Attribution required. Commercial use permitted.
+
+## Contact and Links
+
+- Website: [https://zeredata.com](https://zeredata.com)
+- Contact: [engineering@zeredata.com](mailto:engineering@zeredata.com)