You will get a LiDAR point-cloud and 3D perception solution
Project details
I develop technically robust LiDAR and 3D-perception pipelines for robotics, autonomous systems, mapping, industrial applications, and spatial analysis.
Your solution can include point-cloud filtering, registration, clustering, visualization, 3D detection, semantic segmentation, tracking, camera–LiDAR calibration, coordinate transformations, bird’s-eye-view perception, and sensor fusion.
I work with tools such as Python, C++, Open3D, PCL, ROS 2, PyTorch, MMDetection3D, KITTI, nuScenes, and Waymo-compatible formats.
With more than 15 years of software-engineering experience, I focus on maintainable source code, reproducible processing, measurable evaluation, clear documentation, and practical integration—not only experimental visualizations.
The implementation will be designed around your sensors, coordinate systems, dataset, operating conditions, target hardware, and required outputs.
Your solution can include point-cloud filtering, registration, clustering, visualization, 3D detection, semantic segmentation, tracking, camera–LiDAR calibration, coordinate transformations, bird’s-eye-view perception, and sensor fusion.
I work with tools such as Python, C++, Open3D, PCL, ROS 2, PyTorch, MMDetection3D, KITTI, nuScenes, and Waymo-compatible formats.
With more than 15 years of software-engineering experience, I focus on maintainable source code, reproducible processing, measurable evaluation, clear documentation, and practical integration—not only experimental visualizations.
The implementation will be designed around your sensors, coordinate systems, dataset, operating conditions, target hardware, and required outputs.
AI Development Type
Deep LearningAI Tools
Keras, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$250
|
Standard
$700
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 15 days |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
About Ayman
Computer Vision Engineer | YOLO26, LiDAR, 3D Vision & Edge Deployment
Alex, Egypt - 3:50 pm local time
Computer Vision Capabilities:
• Object detection with YOLO26, YOLO11, YOLOE and RT-DETR
• Semantic, instance and panoptic segmentation
• SAM 3 promptable image/video segmentation and tracking
• Open-vocabulary detection and segmentation
• Multi-object tracking, re-identification and video analytics
• Pose estimation, keypoint detection and activity analysis
• Oriented object detection for aerial and industrial imagery
• OCR, document vision and structured visual extraction
• Defect detection and automated visual inspection
• Face detection, recognition, verification and liveness detection
• Image enhancement, restoration and super-resolution
• Optical flow, motion estimation and scene-flow analysis
• Monocular/stereo depth estimation and depth completion
• Camera calibration, feature matching and visual localization
• 3D reconstruction, photogrammetry and multi-view geometry
LiDAR and 3D Perception:
• Point-cloud processing, filtering, registration and segmentation
• LiDAR-based 3D object detection and tracking
• PointPillars, CenterPoint, PointNet++, SECOND and PV-RCNN
• Bird’s-eye-view perception and occupancy mapping
• Camera–LiDAR–radar calibration and sensor fusion
• BEVFusion and multimodal 3D perception
• Visual SLAM, LiDAR SLAM and visual-inertial odometry
• NeRF and 3D Gaussian Splatting
• KITTI, nuScenes, Waymo and custom 3D datasets
Technologies and Deployment:
• Python, C#, C++, OpenCV, PyTorch and Ultralytics
• Detectron2, MMDetection and MMDetection3D
• NVIDIA Jetson, DeepStream, Isaac ROS and ROS 2
• ONNX, TensorRT, CUDA, OpenVINO, CoreML and TFLite
• Real-time processing with FFmpeg and GStreamer
• Cloud deployment, APIs and enterprise application integration
• SQL Server, database design and data management
• Desktop integration using C# and WPF
My extensive software-engineering background enables me to move beyond experimental prototypes and deliver maintainable applications, APIs, data pipelines, user interfaces, and deployment-ready vision systems.
I am currently pursuing an MSc in Computer Science, strengthening my academic and practical expertise in advanced computing and scalable system development.
Certifications:
• MCTS — Microsoft Certified Technology Specialist
• MCPD — Microsoft Certified Professional Developer
Steps for completing your project
After purchasing the project, send requirements so Ayman can start the project.
Delivery time starts when Ayman receives requirements from you.
Ayman works on your project following the steps below.
Revisions may occur after the delivery date.
Develop, validate, and deliver the LiDAR perception pipeline
I review the sensor data, prepare point clouds and calibration files, build and validate the agreed 3D perception pipeline, then deliver the code, configurations, results, setup instructions, and documentation.