Category: End to End Workflow

LiDAR sensors capture the physical world as dense 3D point cloud data, enabling machines to understand depth, object geometry, and spatial relationships. However, raw LiDAR scans must be structured through annotation before they can be used to train AI models. This article explores the LiDAR / 3D Point Cloud annotation workflow in JTheta.ai Annotate, covering workspace setup, dataset upload, annotation configuration, 3D bounding box labeling, quality review, and exporting datasets in standard formats for autonomous AI systems.

General image annotation is the foundation of reliable computer vision systems—but scaling it requires more than basic labeling tools. This guide walks through JTheta.ai’s end-to-end general image annotation workflow, covering workspace setup, dataset ingestion, annotation configuration, AI-assisted labeling, review pipelines, and training-ready exports. Learn how structured workflows and built-in quality controls help teams move from raw images to production-ready datasets with speed and precision.

Introduction
High-quality AI depends on high-quality annotated data. But how does raw data turn into structured, labeled datasets ready for machine learning? At [Your Company Name], we provide a seamless end-to-end annotation workflow — from project creation with datasets to final export.

Here’s how it works:

🔹 Step 1: Create a Project & Add Your Dataset

The workflow begins by creating a new annotation project.

Upload or link your dataset directly within the project setup.

Add multiple datasets if needed for comparison or multi-source training.

Ensure secure and organized dataset management right from the start.

🔹 Step 2: Define Classes & Annotation Types

During project creation, you can also set up the annotation schema:

Define class labels (e.g., Car, Person, Building, Tumor).

Choose annotation types such as bounding boxes, polygons, keypoints, or segmentation masks.

Standardizing classes at this stage ensures annotation consistency.

🔹 Step 3: Assign Annotators & Reviewers

Once the project is ready, tasks are distributed:

Annotators label the images or scans.

Reviewers validate annotations for quality.

Role-based workflows ensure accountability and collaboration.

🔹 Step 4: Annotate with AI-Assist + Human Precision

This is where the real annotation happens:

AI-Assist tools pre-label objects, speeding up repetitive tasks.

Annotators refine results with manual adjustments for accuracy.

Works across all domains: medical scans, satellite images, LiDAR point clouds, and general images.