Computer vision and 3D sensing pipelines for industrial inspection, robot perception, and spatial computing — from OpenCV image processing to deep learning inference and point cloud reconstruction, deployed on embedded hardware or cloud.
Computer vision and 3D processing capabilities from pixel-level analysis to real-time spatial computing
Custom image processing workflows — filtering, morphology, edge detection, colour segmentation, template matching, OCR, and barcode/QR decoding for industrial inspection and quality control.
Object detection (YOLOv8, SSD), image classification, semantic segmentation, and pose estimation — trained on custom datasets and optimised for edge deployment with TensorRT, TFLite, or ONNX Runtime.
3D point cloud acquisition, filtering, registration, surface reconstruction and feature extraction using PCL and Open3D — from depth cameras, structured light scanners, and LiDAR sensors.
Visual-inertial SLAM, LiDAR SLAM, and multi-sensor fusion for autonomous navigation, indoor mapping, and spatial computing — ORB-SLAM, RTAB-Map, and custom factor-graph solutions.
Camera calibration, multi-camera synchronisation, depth sensor integration (Intel RealSense, ZED, Azure Kinect), and LiDAR point cloud streaming — from hardware selection to calibrated data pipeline.
Automated defect detection, dimensional measurement, surface quality analysis, and part classification on production lines — deployed on Jetson, Raspberry Pi, or industrial PCs with camera trigger and PLC integration.
Our vision and 3D processing tools, frameworks, and deployment platforms
A structured workflow from feasibility study to deployed, validated vision system
We assess your visual task — lighting conditions, object variability, speed requirements, accuracy targets. The output is a sensor recommendation (camera type, resolution, depth sensor, lens), prototype data capture plan, and success criteria definition.
On-site or lab-based image/point cloud capture with controlled lighting. Dataset curation, annotation (bounding boxes, segmentation masks, 3D labels) using CVAT or Label Studio, and train/val/test split strategy.
Classical CV pipeline design (OpenCV) and/or deep learning model training (YOLO, segmentation, classification). Hyperparameter tuning, cross-validation, and iterative improvement until accuracy targets are met on the validation set.
Model quantisation (INT8/FP16), TensorRT or TFLite conversion, and deployment on target edge hardware (Jetson, RPi, industrial PC). Pipeline latency profiling and memory optimisation to meet real-time throughput requirements.
End-to-end system integration — camera trigger, image acquisition, inference, result communication (MQTT, REST, GPIO/PLC). Stress testing under production lighting and environmental conditions with accuracy and throughput logging.
Production deployment with monitoring dashboards for inference accuracy, drift detection, and throughput metrics. Model retraining pipeline documentation and handover of full source, trained weights, and deployment scripts.
A complete, production-ready vision system — algorithms, trained models, edge deployment, and documentation
Exported model weights in ONNX, TFLite, and TensorRT formats with training logs and accuracy metrics
Complete Git repository — image pipeline, training scripts, inference code, and deployment configuration
Curated, labelled dataset in COCO/PASCAL format with augmentation scripts and train/val/test splits
Point cloud acquisition, filtering, registration, and reconstruction scripts with calibration parameters
Docker container or systemd service for Jetson / RPi with auto-start, watchdog, and OTA model update
REST/MQTT endpoint specs for inference results, health checks, and model versioning interfaces
Confusion matrices, precision/recall curves, latency benchmarks, and field validation test results
Retraining runbook, dataset expansion guide, hardware replacement procedure, and monitoring setup
Our primary vision, 3D, and deep learning development stack
Our foundation for all image processing — filtering, morphology, contour analysis, camera calibration, ArUco detection, and custom pipeline orchestration with NumPy and scikit-image.
Object detection, segmentation, and classification model training — Ultralytics YOLOv8, TensorFlow 2.x with Keras, and PyTorch with custom architectures. Model export to ONNX for cross-platform deployment.
Point cloud filtering, ICP registration, RANSAC plane fitting, surface reconstruction, mesh generation, and visualisation — for depth cameras, structured light, and LiDAR data processing.
Depth camera integration (D435i, L515), LiDAR point cloud streaming (Velodyne, Livox, RPLiDAR), multi-sensor calibration, and synchronised RGB-D acquisition pipelines.
GPU-accelerated edge deployment on Jetson Orin, Orin Nano, and Xavier NX — TensorRT INT8/FP16 optimisation, DeepStream video analytics, and CUDA kernel profiling for real-time throughput.
ROS 2 integration for robot vision — ORB-SLAM3, RTAB-Map, Nav2 navigation stack, and MoveIt2 manipulation planning with real-time point cloud and camera feed processing.
Tell us about your visual inspection, 3D sensing, or robot perception challenge — we'll assess feasibility and deliver a detailed technical proposal within 24 hours.
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