LiDAR & 3D Point Cloud Annotation

This task sculpts the world in 3D—think “tree” outlined in a point cloud or “curb” tagged in a LiDAR scan (e.g., “car” boxed, “slope” traced)—to give AI a depth map. Our team annotates these layers, guiding autonomous systems through real space with pinpoint clarity.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are critical in orchestrating the annotation and structuring of data for LiDAR & 3D Point Cloud Annotation within transportation AI workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label datasets that enhance AI’s ability to interpret 3D spatial environments accurately.

Training and Onboarding

PMs design and implement training programs to ensure workers master 3D object tagging, structure outlining, and terrain classification. For example, they might train teams to box “pole” in a LiDAR frame or trace “road edge” in a point cloud, guided by sample scans and spatial standards. Onboarding includes hands-on tasks like annotating 3D data, feedback loops, and calibration sessions to align outputs with AI navigation goals. PMs also establish workflows, such as multi-angle reviews for dense point clouds.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 15,000 LiDAR frames) and set metrics like object accuracy, depth precision, or feature consistency. They track progress via dashboards, address labeling errors, and refine methods based on worker insights or evolving spatial needs.

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high fidelity for small obstacles) into actionable annotation tasks. They also manage timelines, ensuring labeled datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The annotators, taggers, or 3D analysts perform the detailed work of labeling and structuring LiDAR datasets for AI training. Their efforts are spatial and technical, requiring precision and 3D visualization skills.

Labeling and Tagging

For LiDAR data, we might tag objects as “building” or “bike.” In complex tasks, they label features like “overhang” or “ground plane.”

Contextual Analysis

Our team decodes scans, boxing “truck” in a point cloud or tracing “path” in terrain, ensuring AI grasps every dimension of the scene.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “wall” as “fence”) or unclear points (e.g., sparse data), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like occlusions or reflective surfaces—often requiring multi-frame analysis or escalation to LiDAR experts.

We can quickly adapt to and operate within our clients’ LiDAR platforms, such as proprietary 3D tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of frames per shift, depending on the complexity of the point clouds and annotations.

Data Volumes Needed to Improve AI

The volume of annotated LiDAR data required to enhance AI systems varies based on the diversity of environments and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional 3D model might require 5,000–20,000 annotated frames per category (e.g., 20,000 urban scans). For varied or dense scenes, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 frames per issue (e.g., missed objects) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., city-wide autonomy) require datasets in the hundreds of thousands to handle edge cases, rare structures, or new terrains. An annotation effort might start with 100,000 frames, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky frames for further labeling. This reduces total volume but requires ongoing effort—perhaps 500–2,000 frames weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and spatial precision across datasets.

Multilingual & Multicultural LiDAR & 3D Point Cloud Annotation

We can assist you with LiDAR and 3D point cloud annotation across diverse linguistic and cultural landscapes.

Our team is equipped to label and analyze 3D data from global environments, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: