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README.md

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+# 3D Object Detection Dataset
+
+## Overview
+
+- **Source:** ROS2 bags → LakeFS (astraldb + ros2bags)
+- **Data type:** LiDAR (merged point clouds) + Camera (RGB)
+- **Size:** 245.7 GB, 1,194 ZIP archives
+- **Files:** 61,480 PCD + 3,619 JPG
+- **Annotations:** 735,483 3D bounding boxes
+- **Sessions:** 101 unique bag_id (ROS2 bag sessions)
+- **Camera resolution:** 2880×1860
+
+## Structure
+
+```
+3d objects detection/
+  archive.zip                   # All data archives (1194 zips)
+  merged_result.parquet         # Main annotation table
+  tf.parquet                    # Transform tree
+  camera_info.parquet           # Camera calibration
+  archive_index.parquet         # Archive lookup index
+
+  archive/
+    data_0000.zip
+    data_0001.zip
+    ...
+    data_1193.zip
+      pcd/{uuid}.pcd            # Point cloud
+      jpg/{uuid}.jpg            # Camera frame (if exists, 2880×1860)
+```
+
+Each archive `data_NNNN.zip` is ≈200 MB. Files inside are from a single temporal segment.
+
+## Tables
+
+### `merged_result.parquet`
+Main annotation table (735,483 rows).
+
+| Column | Type | Description |
+|---|---|---|
+| `frame_id` | `str` | Frame UUID |
+| `label` | `str` | Object class (car, pedestrian, cyclist, truck, bus, ...) |
+| `data` | `list[float]` | 3D bounding box: [x, y, z, lx, ly, lz, roll, pitch, yaw] |
+| `main_timestamp` | `int` | Timestamp in nanoseconds |
+| `bag_id` | `str` | ROS2 bag session UUID |
+| `archive` | `str` | ZIP archive name (`data_NNNN.zip`) |
+| `pcd_path` | `str` | Path inside archive (`pcd/{uuid}.pcd`) |
+| `jpg_path` | `str` | Path inside archive (`jpg/{uuid}.jpg`) or `null` |
+
+### `tf.parquet`
+Transform tree (976,208 rows). Use for converting between coordinate frames.
+
+| Column | Description |
+|---|---|
+| `timestamp` | Nanoseconds |
+| `from_frame`, `to_frame` | Coordinate frames |
+| `tx, ty, tz, qx, qy, qz, qw` | Translation + rotation (quaternion) |
+
+### `camera_info.parquet`
+Camera calibration (61,480 rows). Use for projecting 3D boxes onto images.
+
+| Column | Description |
+|---|---|
+| `_timestamp` | Nanoseconds |
+| `height`, `width` | 1860×2880 |
+| `k` | Intrinsic matrix (3×3) |
+| `d` | Distortion coefficients |
+| `r` | Rectification matrix |
+| `p` | Projection matrix |
+
+### `archive_index.parquet`
+Index for O(1) lookup of which archive contains which data.
+
+| Column | Description |
+|---|---|
+| `archive` | ZIP name |
+| `size_mb` | Archive size |
+| `file_count`, `pcd_count`, `jpg_count` | File counts |
+| `bag_ids` | Session IDs in archive |
+| `min_timestamp`, `max_timestamp` | Temporal range |
+
+## Data Quality
+
+- **Black frames removed:** 404 JPGs with brightness < 30 (camera glitch) were removed from archives `data_0902`–`data_0910`
+- **PCD validation:** All 61,480 PCD files passed header validation (VERSION 0.7, FIELDS, POINTS > 0)
+- **NaN in `jpg_path`:** 720,487 rows have no camera frame (lidar-only frames)
+
+## Usage
+
+```python
+import pandas as pd
+import zipfile
+
+# Load annotations
+df = pd.read_parquet("merged_result.parquet")
+
+# Read a specific point cloud
+row = df.iloc[0]
+with zipfile.ZipFile(f"archive/{row['archive']}") as z:
+    pcd_data = z.read(row['pcd_path'])  # bytes → parse as PCD
+    if row['jpg_path'] is not None:
+        jpg_data = z.read(row['jpg_path'])  # bytes → decode with PIL
+
+# Load transforms
+tf = pd.read_parquet("tf.parquet")
+# Filter by timestamp frame for coordinate conversion
+
+# Load camera calibration
+cam = pd.read_parquet("camera_info.parquet")
+```
+
+## Coordinate System
+
+- Bounding boxes are in `base_link` (LiDAR) coordinate frame
+- Use `tf.parquet` to convert between `base_link`, `gps`, and other frames
+- Camera intrinsics and extrinsics in `camera_info.parquet`
+
+## License
+
+Proprietary. All rights reserved.