How to Scale 3D Segmentation Pipelines Across Distributed Teams

The safety and accuracy of Autonomous Driving Systems (ADAS/AV) hinge on perception quality. Modern vehicles ingest enormous volumes of LiDAR, RADAR, and camera data, often reaching petabyte scale across fleets. Yet transforming this raw sensor output into high-fidelity, production-ready training data remains expensive and operationally complex.

While model architectures and training infrastructure continue to mature, perception teams are frequently stuck by fragmented annotation workflows, manual coordination, and tools that were never designed for large-scale 3D data.

The cost of inefficiency is real: thousands of engineering hours lost to file handling, rework caused by inconsistent labels, and endless back-and-forth between distributed labeling and QA teams. As AV programs scale globally, these inefficiencies compound.

The goal is clear: move from siloed, ad hoc labeling efforts to a unified, distributed factory for 3D segmentation. One that can scale across teams, geographies, and data volumes without sacrificing quality or security.

The Core Pain Points in Distributed 3D Annotation

Scaling 3D annotation is not simply a matter of hiring more annotators. Distributed teams introduce a unique set of operational challenges that traditional tooling struggles to address.

Infrastructure silos are often the first hurdle. LiDAR point clouds are massive, and sharing them across global teams typically involves slow downloads, duplicated storage, or risky data transfers. Security constraints further complicate access, especially when sensitive AV data must remain within tightly controlled environments.

Next comes orchestration chaos. In a multi-time-zone setup, it quickly becomes unclear who is labeling which scenes, who is reviewing them, and what stage each dataset is in. Without a centralized source of truth, project managers resort to spreadsheets, Slack messages, and manual status checks—none of which scale.

Finally, there is quality at scale. Safety-critical ADAS and AV models demand extremely high precision. Maintaining consistent labeling standards across outsourced workforces, remote contractors, and internal QA teams is notoriously difficult, particularly in 3D where errors can be subtle but catastrophic.

Strategy 1: Decoupling Storage from Access

One of the most effective ways to scale distributed 3D annotation is to decouple where data is stored from how it is accessed.

With Encord’s Cloud Integration, raw LiDAR and RADAR data can remain securely in your private infrastructure, whether that’s AWS S3, Google Cloud Storage, or Azure Blob Storage. Annotators and reviewers interact with the data through a high-performance, web-based 3D visualization layer.

To learn more about how Encord supports the features below, you can explore the corresponding documentation: 

• Encord integrates with AWS S3, GCP Storage, Azure Blob, and other S3‑compatible providers via data integrations. View: Data integrations
• When you “register” cloud data (including point cloud data) in Encord, only references/paths are stored; your data is not stored on Encord servers. View: Data management
• Point cloud (LiDAR) data is supported as a first‑class modality (e.g. .pcd, .ply, .las, .laz, .mcap, .bag, .db3) and is handled as “Scenes” for annotation. View: Supported data
• For standard cloud integrations, Encord accesses media via signed URLs with configurable expiry, and CORS is required so the browser can load data directly from your bucket. View: AWS integration & Data integrations

This approach delivers two critical benefits. 

First, it eliminates latency and bandwidth bottlenecks for global teams, enabling near-instant loading of massive point clouds. 

Second, it preserves strict data security and compliance requirements by ensuring sensitive sensor data never leaves your controlled environment.

Strategy 2: Modular Workflows for Global Coordination

Scaling annotation requires structure. Rather than treating 3D segmentation as a single task, high-performing teams break it into modular, well-defined stages.

For example, a typical pipeline might include:

• Ground plane detection
• 3D object segmentation and tracking
• Attribute tagging and metadata enrichment

Encord uses workflows composed of multiple stages (Start, Annotate, Review, Routers, Complete), and each stage is configured and managed as a discrete step in the workflow canvas. You can then assign different collaborators to different workflow stages (e.g., specific annotators to an Annotate stage, specific reviewers to a Review stage), which is effectively task orchestration across stages. 

Additionally, role-based access control (RBAC) at the project level (Admin, Team Manager, Annotator, Reviewer, Annotator & Reviewer) ensures that the right teams, based on expertise, location, or trust level, are assigned to the right tasks. 

This helps AV and ADAS teams organize and orchestrate different tasks so that they can assign different people to different stages and have visibility into who is involved at each point.

The result is a transparent, auditable workflow where progress is visible in real time and handoffs between teams are seamless.

Strategy 3: Multi-Layered QA and Consensus

Quality assurance cannot be an afterthought in 3D perception, especially for safety-critical applications.

A common best practice is a two-phase review model. In the first phase, a distributed workforce handles initial labeling at scale. In the second phase, internal domain experts perform targeted reviews and final sign-off.

Encord supports this layered approach while keeping the process efficient with multi‑stage workflows that have multiple review phases. You can configure workflows with one or more Annotate and Review stages, and assign different collaborator groups (e.g., external workforce vs. internal experts) to each stage. You can also have multiple review stages and even “tie‑breaker” review stages, which aligns with a layered review model where later stages are handled by more expert reviewers. 

Consensus workflows explicitly support multiple annotators and reviewers, with reviewers comparing annotators’ labels and deciding on the best labels or on agreement. This is designed for quality control across multiple annotators, making it easy to flag misaligned bounding boxes or segmentation errors that would otherwise go unnoticed.

For example, the distributed team could be in the first phase and an internal QA member in the second, keeping the process simple but rigorous.

This combination of human expertise and automated checks ensures sub-centimeter accuracy without slowing down throughput.

Strategy 4: Leveraging Automation to Assist Distributed Teams

Automation is a force multiplier for distributed annotation teams, especially in 3D.

Features like interpolation and object tracking for videos and image sequences allow annotators to automatically create instance labels by estimating the location that labels should be created in videos and image sequences between manually labeled frames. This dramatically reduces manual effort while improving temporal consistency, even when teams are working asynchronously across time zones.

Beyond annotation speed, Encord enables active learning loops. By surfacing rare edge cases, adverse weather conditions, or unusual traffic scenarios teams can prioritize the most valuable data for their most experienced reviewers.

Encord Active uses quality metrics and acquisition functions to:

• find failure modes
• find label errors
• prioritize high‑value data for relabeling
• and support active learning loops

This ensures that expert attention is focused where it has the greatest impact on model performance.

Speeding Up the Road to Autonomy

Scaling 3D segmentation pipelines isn’t about throwing more people at the problem. It’s about building an intelligent, distributed infrastructure that unifies storage, orchestration, quality, and automation.

By leveraging Encord’s specialized 3D tools, AV teams can transform fragmented annotation efforts into a coordinated, high-throughput production line, while also cutting engineering overhead.

Ready to see how Encord handles LiDAR point clouds at scale? Learn more.