You will get AI 3D Scanning & Reconstruction System | LiDAR & Computer Vision
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Project details
I design and deploy production-ready 3D computer vision systems, from depth estimation and point cloud processing to full 3D reconstruction, mesh generation, and spatial analysis pipelines.
My team has delivered 3D CV systems across construction & architecture (building reconstruction from drone imagery), e-commerce & fashion (3D product scanning and visualization), healthcare & medtech (body scanning, surgical planning), robotics (3D perception, SLAM, object localization), and industrial inspection (defect detection, dimensional analysis).
Core technical capabilities:
— Depth estimation: monocular, stereo, LiDAR-based pipelines
— Point cloud processing, filtering, segmentation, registration
— 3D reconstruction: SfM, MVS, NeRF, Gaussian Splatting
— Mesh generation, surface reconstruction, textured 3D model export
— Real-time 3D inference with TensorRT and ONNX optimization
— Edge and cloud deployment: AWS, GCP, Docker, FastAPI
Tech stack: PyTorch, Open3D, PCL, OpenCV, COLMAP, Dust3r, TensorRT, ONNX.
Each delivery includes source code, validation results, API documentation, and deployment guide — ready for integration into your existing infrastructure.
My team has delivered 3D CV systems across construction & architecture (building reconstruction from drone imagery), e-commerce & fashion (3D product scanning and visualization), healthcare & medtech (body scanning, surgical planning), robotics (3D perception, SLAM, object localization), and industrial inspection (defect detection, dimensional analysis).
Core technical capabilities:
— Depth estimation: monocular, stereo, LiDAR-based pipelines
— Point cloud processing, filtering, segmentation, registration
— 3D reconstruction: SfM, MVS, NeRF, Gaussian Splatting
— Mesh generation, surface reconstruction, textured 3D model export
— Real-time 3D inference with TensorRT and ONNX optimization
— Edge and cloud deployment: AWS, GCP, Docker, FastAPI
Tech stack: PyTorch, Open3D, PCL, OpenCV, COLMAP, Dust3r, TensorRT, ONNX.
Each delivery includes source code, validation results, API documentation, and deployment guide — ready for integration into your existing infrastructure.
Machine Learning Tools
Amazon SageMaker, Keras, MLflow, NumPy, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, Python, PyTorch, TensorFlowWhat's included $15,000
These options are included with the project scope.
$15,000
- Delivery Time 30 days
- Number of Revisions 1
- Number of Model Variations 1
- Number of Scenarios 1
- Number of Graphs/Charts 0
- Model Validation/Testing
- Model Documentation
- Source Code
Frequently asked questions
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CJ
Clayton J.
Apr 4, 2026
AI audio engineer (Deep Learning) – SAM Audio powered mastering engine
Oleg delivered an outstanding implementation of our SAM Audio-powered AI mastering engine. He demonstrated strong Python expertise and deep knowledge of Machine Learning and Artificial Neural Networks, building a robust segmentation pipeline and integrating it seamlessly into our mastering workflow. His Deep Learning models for audio analysis were well-structured, efficiently trained, and optimized for production inference. Beyond the technical execution, Oleg showed real understanding of DSP and mastering practices, which made the final system both technically impressive and musically reliable. Communication was clear, deadlines were met, and the overall quality exceeded expectations.
AC
Arina C.
Mar 21, 2026
30 minute consultation
Really efficient prep before the call, punctual and prepared. Thanks!
WR
William R.
Oct 22, 2025
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The team carried out the project with efficiency and professionalism. They were highly attentive to changes and communicated progress clearly throughout. I highly recommend ItJim to anyone seeking expert engineers for CV, gen AI, and ML solutions.
NM
Nicolas M.
Oct 13, 2025
Expert in Stable Diffusion for Image Creation
Awesome work!
JP
Jameson P.
Sep 29, 2025
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They completed the work ask requested.
About Oleg
Generative AI, Computer Vision, Audio & Music AI Engineer
100%
Job Success
Kyiv, Ukraine - 9:59 am local time
I design and deploy production-grade AI solutions built from scratch for image, video, audio, and multimodal applications.
My work focuses on system-level development: from architecture design and model training to optimization and deployment, delivering scalable AI components ready for real-world environments.
Core Expertise
Generative AI Systems
▪️ Diffusion-based image and video generation (SDXL, FLUX, ControlNet)
▪️ Controlled generation, inpainting, style transfer
▪️ Text-to-video and 3D generation pipelines
▪️ Neural speech synthesis and AI audio generation
▪️ Synthetic data generation for model training
Computer Vision
▪️ Object detection, tracking, and segmentation
▪️ OCR, pose estimation, face recognition
▪️ 3D reconstruction and depth estimation
▪️ Real-time and edge-optimized vision systems
Audio AI & DSP
▪️ Audio segmentation and source separation
▪️ Spectrogram-based modeling and transformer audio embeddings
▪️ Speech recognition and synthesis systems
▪️ Feature-driven audio analysis pipelines
▪️ Cross-modal (audio–vision–text) modeling
Machine Learning & Architecture
▪️ Custom neural network architecture design (CNNs, U-Nets, Transformers)
▪️ End-to-end ML lifecycle: data strategy → training → validation → deployment
▪️ Performance optimization for accuracy, latency, and hardware constraints
Deployment & Edge Systems
▪️ Cloud, on-premise, and edge deployment
▪️ Dockerized inference services
▪️ ONNX / TensorRT optimization
▪️ Mobile and embedded AI systems
What I Deliver
▪️ Custom AI system development (from scratch)
▪️ Structured R&D and technical validation
▪️ Production-ready ML / CV / multimodal pipelines
▪️ Scalable architecture aligned with product and business goals
Steps for completing your project
After purchasing the project, send requirements so Oleg can start the project.
Delivery time starts when Oleg receives requirements from you.
Oleg works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements analysis & feasibility study
Review input data, define 3D reconstruction pipeline architecture, select optimal depth estimation and point cloud processing approach.
Data pipeline setup
Prepare and validate input data (images, LiDAR scans, video), implement preprocessing, calibration, and data augmentation for 3D model training.