3D Bounding Cuboid Annotations
Volumetric 3D bounding box extent, centroid coordinate, and orientation yaw angle labeling for vehicles, pedestrians, and warehouse assets.
Quality & Precision Benchmarks
3D IOU ACCURACY
> 88.4%
CENTROID POSITION ERROR
< 1.8 cm
YAW ANGLE DRIFT
< 0.5°
FORMAT COMPATIBILITY
KITTI / NuScenes / COCO 3D
Dataset Taxonomy & Output Structure
center_x, center_y, center_z (Centroid Coordinates in Meters)
size_dx, size_dy, size_dz (Volumetric Extent in Meters)
yaw_rot, pitch_rot, roll_rot (Orientation Angles in Radians)
num_points_inside (Point Cloud Density Count)
Specific Type Tasks & Applications
- • Autonomous Vehicle 3D Object Tracking
- • Warehouse AMR Pallet & Forklift Detection
- • Industrial Plant Spatial Distance Estimation
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Loose bounding cuboids ignoring ground plane alignment | PASS: Ground-truth tight-fit 3D cuboids with exact orientation yaw angle |
| FAIL: 2D bounding box projections lacking 3D depth extent | PASS: 3D point cloud volumetric extent annotation with intensity reflectivity |
Files & Telemetry Data Example (Python `open3d`)
import open3d as o3d
import numpy as np
# Load Blue Projects 3D LiDAR Type Dataset: 3D Bounding Cuboid Annotations
pcd = o3d.io.read_point_cloud("3d-cuboid-annotation_frame_001.pcd")
print("Loaded Point Cloud Array Shape:", np.asarray(pcd.points).shape)
Why Blue Projects for 3D Bounding Cuboid Annotations?
Request a free matched 10-scene sample batch formatted to your exact LiDAR sensor or ADAS perception model requirements.
Request Free Sample Batch →