This guide compares the eight LiDAR annotation platforms most often shortlisted by autonomous driving, ADAS, and robotics teams in 2026. We list each platform honestly, including where it's strong and where it isn't, and we include our own platform with the same lens. Use the comparison table at the bottom for a quick read.
We focused on capabilities that matter for production LiDAR annotation programs:
For platforms outside our direct experience, we sourced details from their public documentation, customer reviews on G2 and TrustRadius, the ENX TISAX portal for certification status, and third-party comparisons. Where we could not verify a claim, we say so.
Full disclosure: Kognic is the publisher of this guide. We compared ourselves on the same criteria.
Kognic is a LiDAR and sensor-fusion annotation platform purpose-built for autonomous driving, ADAS, and robotics. Native 3D point cloud editing has been the core of the product for seven years, with calibrated camera + LiDAR + radar fusion, multi-LiDAR support, temporal sequence handling with ego-motion compensation, and 90+ automated quality checkers tuned for AV data.
Customers include Zenseact, Continental, Bosch, ZF, Qualcomm, Kodiak, Einride, and Gatik. Over 100 million annotations delivered across 120+ programs.
Strengths: Deepest native LiDAR + sensor fusion stack on the market for AV. Managed annotation workforce trained on autonomous driving scenarios. TISAX Level 3 and ISO 27001 certified. API-first with 25+ documented endpoints and a Python SDK on PyPI. Language Grounding capability for VLM and VLA model training.
Where it's not the obvious choice: Kognic does not annotate audio data or general-purpose computer vision (medical imaging, document AI, retail). For teams whose primary work is outside autonomous driving, a broader multimodal platform may fit better. Kognic also does not market a standalone data curation product or a packaged active learning UI in the way Encord does.
Pricing: Enterprise, project-based. Not self-serve.
Best for: AV teams running ADAS, full autonomy, or robotics programs with multi-sensor data and European OEM contracts.
Encord is a multimodal AI data labeling platform that covers computer vision, document AI, medical imaging, audio, and now 3D LiDAR. The platform includes Encord Active for model evaluation and Encord Index for data curation.
Strengths: Broad data type support beyond autonomous driving. Active learning and data curation as marketed products. Good general-purpose annotation features.
Where it's not the obvious choice: 3D and sensor fusion support is newer (added in 2024), so the depth and maturity is less than purpose-built AV platforms. Encord positions itself across many verticals, which means the AV-specific quality assurance and workflow tooling tends to be shallower. We also documented several inaccuracies in Encord's public comparison page about Kognic; teams evaluating Encord should verify claims directly against vendor documentation.
Pricing: Tiered subscription plus enterprise. Self-serve start available.
Best for: Teams working across multiple data domains where 3D and sensor fusion are not the dominant use case.
See our Kognic vs Encord comparison for a deeper claim-by-claim look.
Scale AI is a large general-purpose data labeling platform. Originally focused on autonomous vehicle annotation, the company has pivoted toward LLM RLHF, generative AI evaluation, and defense applications in recent years.
Strengths: Operational scale for high-volume labeling. Strong on 2D image and text annotation. Established brand among enterprise buyers.
Where it's not the obvious choice: Scale's strategic focus has moved away from autonomous driving annotation toward defense and LLM RLHF. Their AFM-1 foundation model is 2D-only. For teams whose core need is multi-sensor 3D annotation for AV programs, the product roadmap signal is mixed.
Pricing: Enterprise. Not self-serve for AV work.
Best for: Large enterprises with mixed labeling needs across image, text, and language model evaluation.
See our Kognic vs Scale AI comparison.
Labelbox is a general-purpose AI labeling platform covering 2D images, video, text, and 3D point clouds. The company has emphasized LLM evaluation and RLHF in recent product directions.
Strengths: Polished UX for 2D image and document annotation. Established platform with broad data type support. Freemium tier available for small teams.
Where it's not the obvious choice: 3D point cloud and sensor fusion support is adapted from 2D tooling rather than purpose-built. For multi-sensor AV programs, the calibration awareness and cross-sensor consistency features are less developed than purpose-built platforms.
Pricing: Labelbox Units (proprietary bundled metric) plus enterprise contracts. Freemium tier for small teams.
Best for: Mixed labeling needs across multiple data types where 3D is occasional rather than central.
See our Kognic vs Labelbox comparison.
Segments.ai is a point cloud-focused labeling platform with strong 3D segmentation and bounding box tooling. Owned by Foretellix as of 2024.
Strengths: Purpose-built for 3D point cloud annotation. Good developer experience with API and SDK. Reasonable pricing for smaller teams.
Where it's not the obvious choice: Sensor fusion depth is shallower than the deepest AV-focused platforms. Smaller workforce options compared to platforms with managed services. For European OEM programs requiring TISAX, verify status directly on the ENX portal.
Pricing: Subscription-based with usage tiers.
Best for: Robotics, mining, drone, or smaller AV teams that need solid 3D point cloud tools without a managed workforce.
Supervisely is a labeling platform that supports a wide range of data types including 3D point clouds, with an emphasis on customization and self-hosting options for enterprises with strict data residency requirements.
Strengths: Self-hosted deployment is a real option, which matters for teams with strict data residency. App ecosystem allows custom tooling and ML model integration. Multiple data types supported.
Where it's not the obvious choice: Annotation services are not the company's core offering, so workforce scale is typically your responsibility. 3D and sensor fusion features are competent but not as deep as platforms specifically built for AV multi-sensor work.
Pricing: Free community tier, paid plans for enterprise.
Best for: Teams with strong in-house annotation workforces and a need for self-hosting or heavy customization.
BasicAI is a cloud-based annotation platform that supports 2D images, video, and 3D LiDAR. The platform emphasizes ease of use and supports common AV annotation types.
Strengths: Straightforward onboarding. Supports the common AV annotation types (cuboids, polygons, semantic segmentation). Reasonable pricing for smaller teams.
Where it's not the obvious choice: Multi-sensor fusion depth, advanced calibration features, and large-scale temporal sequence handling are not as developed as in purpose-built AV platforms. Workforce options at fleet scale tend to be limited.
Pricing: Subscription, usage-based.
Best for: Smaller AV teams or robotics teams that need basic multi-modal labeling without a heavy enterprise procurement process.
CVAT (Computer Vision Annotation Tool) is the leading open-source annotation tool, originally developed by Intel. It supports a wide range of data types including 3D point clouds, with the option to self-host or use the hosted version.
Strengths: Free and open source. Active community. Wide format support. Self-hosting eliminates vendor data risk concerns.
Where it's not the obvious choice: No managed annotation workforce. No commercial SLA. Limited cross-sensor fusion and advanced AV-specific tooling. Quality assurance and operations are entirely your responsibility. For fleet-scale production work, CVAT alone is not enough; teams typically build significant tooling and workforce on top.
Pricing: Free (open source). Hosted version has paid tiers.
Best for: Academic research, prototypes, internal tools, or teams with strong engineering resources who want full control of the labeling stack.
| Platform | Native 3D | Sensor Fusion | Managed Workforce | TISAX AL3 | API/SDK | Best Fit |
|---|---|---|---|---|---|---|
| Kognic | Yes (7+ yrs) | Native multi-sensor | Yes | Yes | 25+ APIs, Python SDK | AV / ADAS / robotics with EU OEMs |
| Encord | Yes (2024) | Camera + LiDAR sync | Limited | Verify on ENX | API, SDK | Multi-domain CV beyond AV |
| Scale AI | Yes (legacy AV) | Multi-sensor | Yes (large) | Verify on ENX | API | Mixed CV + LLM RLHF |
| Labelbox | Yes (adapted) | Limited | Marketplace | Verify on ENX | API | General-purpose CV + LLM |
| Segments.ai | Yes (specialist) | Limited | No | Verify on ENX | API, SDK | Robotics + smaller AV teams |
| Supervisely | Yes | Limited | No | Self-host option | API, apps | Custom / self-hosted |
| BasicAI | Yes | Limited | Limited | Verify on ENX | API | Small AV teams |
| CVAT | Yes | Limited | No | Self-host option | Open API | Research, prototypes |
"Verify on ENX" means we recommend confirming current TISAX status on the ENX portal at portal.enx.com/en-US/TISAX rather than relying on marketing claims.
Three questions tend to settle the choice.
1. How many sensors do you run, and how complex is the fusion? Single-LiDAR prototypes can use almost any 3D-capable tool. Multi-LiDAR + camera + radar setups at fleet scale narrow the field to platforms with calibration-aware sensor fusion built in. See our Multi-LiDAR annotation guide for why this matters.
2. Do you need tooling only, or tooling plus a workforce? If your team can absorb annotation operations, software-only platforms (Segments.ai, Supervisely, CVAT) are viable. If you need someone to bring annotators trained on AV scenarios, the practical choices narrow to platforms with managed services.
3. Does your data come from European OEMs or programs with strict information security requirements? TISAX Level 3 is effectively required for production European OEM contracts. Most general-purpose labeling platforms do not hold it. See our TISAX guide for the verification process.
The best LiDAR annotation platform depends on your use case. For autonomous driving and ADAS programs with multi-sensor data and European OEM contracts, Kognic is the leading purpose-built choice with seven years of production 3D experience, native sensor fusion, a managed AV-trained workforce, and TISAX Level 3 certification. For multi-domain computer vision beyond AV, Encord offers broader data type support. For self-hosted or open-source workflows, CVAT and Supervisely are the established options. For specialist 3D-only work without a managed workforce, Segments.ai is a solid choice.
Purpose-built 3D platforms (Kognic, Segments.ai) treat point clouds as a first-class data type with calibration-aware projection, native multi-sensor workflows, and AV-specific quality assurance. Retrofitted tools (general-purpose labeling platforms that added 3D later) tend to handle simple point cloud annotation but struggle with multi-sensor fusion, temporal sequences at scale, and the depth of automated QA needed for production AV data.
For production contracts with European OEMs handling sensitive vehicle data, TISAX Assessment Level 3 is effectively required. Vendors without it are typically limited to non-sensitive pilot work, public datasets, or simulation data. The exact requirement depends on the data classification of each project and the OEM's procurement standards. Verify any vendor's status directly on the ENX portal.
LiDAR annotation pricing varies by platform model and project complexity. Open-source tools (CVAT) are free but require your team to absorb workforce and operations costs. Subscription platforms (Encord, Labelbox, Segments.ai) charge per-seat or per-unit fees. Enterprise platforms with managed annotation services (Kognic, Scale AI) typically use project-based pricing tied to deliverables. For multi-LiDAR programs, calibration-aware pipelines that propagate annotations across sensors typically cost less per labeled object than calibration-naive workflows, even if the per-platform fee is higher.
Technically yes, but in practice it's costly. Annotation guidelines, quality criteria, workforce training, and platform-specific tooling all need to be re-established. Most teams pilot two or three platforms with a small dataset before committing, and stick with the chosen platform for the full program. If you need to switch, plan for a 4 to 8 week transition period including re-training and quality re-baselining.
Multi-LiDAR (3+ sensors) is increasingly common in autonomous trucking, robotics, and L4 passenger vehicles. Kognic supports multi-LiDAR natively with calibration-aware fusion across all sensors. Other platforms vary widely in their multi-LiDAR support. See our Multi-LiDAR annotation guide for the capabilities that matter at scale.
Most platforms offer pilot or trial programs. Best practice is to define 3 to 5 representative scenes (or short sequences) from your actual data, define what "good" looks like (precision thresholds, annotation types, edge case handling), and run those same scenes through 2 to 3 shortlisted platforms in parallel. Compare not just throughput but cross-sensor consistency, edge case handling, and how the platform manages calibration drift.
Want to see how Kognic handles your sensor stack and data volumes? Talk to our team about a scoped pilot.