StarLineResearch/3D-Objects-Detection

3D Object Detection Dataset

Overview

• Data type: LiDAR (point clouds) + Camera (RGB images)
• Size: 245.7 GB, 1,194 ZIP archives
• Files: 61,480 PCD + 3,619 JPG
• Annotations: 735,483 3D bounding boxes
• Sessions: 101 unique recording sessions
• Camera resolution: 2880×1860

Structure

.
├── README.md
├── merged_result.parquet         # Main annotation table
├── tf.parquet                    # Transform tree
├── camera_info.parquet           # Camera calibration
├── archive_index.parquet         # Archive lookup index
├── file_catalog.parquet          # File inventory
└── data/
    ├── data_0000.zip
    ├── data_0001.zip
    ├── ...
    └── data_1193.zip
        ├── pcd/{uuid}.pcd        # Point cloud
        └── jpg/{uuid}.jpg        # Camera frame (2880×1860, may be absent)

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	Recording 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

file_catalog.parquet

Inventory of all files inside the 1,194 ZIP archives (65,496 entries).

Column	Type	Description
archive	str	ZIP archive name
path	str	Full path inside archive (pcd/{uuid}.pcd or jpg/{uuid}.jpg)
size	int	Uncompressed file size in bytes
csize	int	Compressed size in bytes

Usage

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"data/{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.