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Pointwise – Self-hosted Lidar annotation for AV teams

Pointwise – Self-hosted Lidar annotation for AV teams

by sohailsaifi·Feb 24, 2026·1 point·0 comments
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AI Analysis

●●●BangerSolve My ProblemWizardrySlick

Self-hosted LiDAR annotation matching cloud tools' UX; solves real AV team pain.

Strengths
  • •WebGL renderer: 10M+ points at 60fps is genuine technical achievement, not just feature count.
  • •Complete self-hosted stack (Docker + PostgreSQL) solves real compliance/privacy pain for AV teams.
  • •Production-ready: multi-user workflows, review pipelines, audit trails—not MVP scaffolding.
Weaknesses
  • •Narrow audience: only relevant to AV/robotics teams with data sovereignty constraints.
  • •No clear pricing or community traction signals yet; competition from Supervisely, Scale AI self-hosted options unclear.
Target Audience

Autonomous vehicle/robotics teams, annotation teams with data privacy requirements

Similar To

Supervisely (annotation platform) · Scale AI (labeling platform) · CVAT (self-hosted annotation)

Post Description

Built this because annotation teams working on serious AV/robotics datasets often can't send data to a third-party cloud, and existing self-hosted options have no real multi-user workflow.

Runs on Docker + PostgreSQL. WebGL renderer handles 1M+ points at 60fps in the browser. Full annotator/reviewer/admin roles with a review pipeline, issue tracking, and audit trails. Supports local filesystem or S3-compatible storage.

Happy to answer questions about the rendering approach or the multi-user architecture.

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