Datasets:

StarLineResearch
/

3D-Objects-Detection

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

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