Processing massive amounts of data at high speed.
Parse, localize, and predict 3D bounding boxes across millions of frames of urban or highway LiDAR sweeps with PointPillars’ pipeline.
Optimize for scale3D & LiDAR
Spatial AI models for 3D bounding-box estimation, point-cloud segmentation, and LiDAR processing.
SAM 3D – universal 3D segmentation model
The model demonstrates an ability to isolate and segment spatial objects within 3D point clouds. It adapts zero-shot 2D foundational knowledge into three-dimensional space, handling objects even in low-density or noisy environments.
// Why SAM 3D at Keylabs- Zero-shot 3D generalization: Segment unknown 3D objects, custom machinery, or road layouts without custom training on specific target classes.
- Spatial labeling: Prompt-based 3D segmentation. Annotators generate a clean 3D mesh or mask from a few sparse points or click-boxes.
- Scene breakdown: Separates closely packed objects or fuzzy boundaries within LiDAR sweeps (overlapping vegetation or dense urban traffic).
PointPillars – production-ready 3D detection
Real-time 3D object detection in point clouds. By turning point clouds into vertical columns (pillars), it processes large autonomous-driving and robotics datasets fast.
// Why PointPillars at Keylabs- Real-time point cloud processing: Built for industrial-scale automotive and geospatial data, analyzing multi-frame LiDAR sequences in seconds.
- 3D bounding boxes: Automatically predicts 3-dimensional bounding boxes (x, y, z, w, l, h, orientation) for vehicles, pedestrians, and infrastructure obstacles.
- Hardware-optimized: Low compute overhead lets pre-labeling pipelines handle terabytes of sensor-fusion data.
Choose your path
High-stakes projects where spatial or navigation errors are costly.
Pair SAM 3D with expert human-in-the-loop validation for accurate 3D semantic segmentation in autonomous driving and robotics.
Ensure accuracyMaximizing ROI on complex or sparse point cloud datasets.
Use active learning pipelines to automatically segment standard assets, routing only the most cluttered, noisy, or low-density LiDAR segments to validators.
Reduce costsEnterprise teams with their own AI technology stacks.
Connect your own 3D detection or sensor-fusion tracking systems to the secure Keylabs environment.
Integrate your AIReady to automate your 3D labeling pipeline?
Talk to our 3D data team to get started.
Custom solution? hello@keylabs.ai
