MLOps 3D Scene Reconstruction

Getting Started

  • Installation Guide
    • Prerequisites
    • Step 1 — Clone the Repository
    • Step 2 — Download the Dataset
    • Docker Setup (Recommended)
      • Step 3a — Configure the Environment File
      • Step 3b — Generate Docker Secrets
      • Step 3c — Generate TLS Certificates
      • Step 3d — Clone External Dependencies
      • Step 3e — Launch the Full Stack
    • Native Python Setup (Developer Mode)
      • Step 3a — Build ASMK
      • Step 3b — Build CroCo / DUSt3R Kernels
      • Step 3c — Build Remaining Packages
      • Step 3d — Create the Python Virtual Environment
    • Pre-trained Model Weights
    • Verifying the Installation
    • Troubleshooting Installation
  • Usage Guide
    • Running the System
      • Docker (Production)
      • Native (Development)
    • Running the Offline DVC Pipeline
    • Uploading Data via the UI
    • API Authentication
    • Uploading Data via the API
    • Polling Job Status
    • Downloading Results
    • Checking Data Drift
    • Triggering Retraining
    • Example End-to-End Workflow
    • Disabling Authentication (Development Only)

User Interface

  • UI Guide
    • Overview of the Interface
    • Step 1 — Logging In
    • Step 2 — The Header
    • Step 3 — The Upload Panel (Left Panel, Top)
    • Step 4 — The Stage Tracker (Left Panel, Middle)
    • Step 5 — The 3D Model Viewer (Right Panel)
    • Step 6 — The Stats Table (Bottom Panel)
    • Step 7 — Downloading Results
    • Step 8 — Starting a New Reconstruction
    • Drift Warning Banner
    • Error States
    • Frequently Asked Questions (UI)

System Reference

  • System Architecture
    • High-Level Architecture
    • Frontend
    • API Gateway and GPU Worker (Ray Serve)
    • Offline MLOps Pipeline
    • Monitoring Stack
    • Data Flow
    • Networking
    • Security Boundaries
  • Pipeline Documentation
    • Pipeline Overview
    • Stage 1 — Data Validation
    • Stage 2 — Exploratory Data Analysis and Baselines
    • Stage 3 — Image Preprocessing
    • Stage 4 — Data Preparation
    • Stage 5 — Scene Reconstruction (Core Pipeline)
      • Shortlist Generation
      • Feature Extraction and Matching
      • COLMAP Incremental SfM
    • Stage 6 — Evaluation
    • Online Inference Pipeline
    • Model Selection and Promotion
    • Drift Monitoring and Retraining
  • API Reference
    • Authentication
    • Endpoint Reference
      • POST /auth/token
      • GET /health
      • GET /ready
      • GET /metrics
      • POST /upload
      • GET /status/{job_id} | GET /jobs/{job_id}
      • GET /download/jobs/{job_id}
      • GET /download/jobs/{job_id}/csv
      • GET /download/jobs/{job_id}/{filename}
      • GET /clusters/{job_id}
      • GET /jobs/{job_id}/insights
      • POST /drift
      • POST /drift/trigger-retrain
    • Error Responses

MLOps & Operations

  • Security & Compliance
    • Overview of Security Controls
    • 1. Docker Secrets Management
    • 2. JWT Bearer Token Authentication
    • 3. Access Logging
    • 4. TLS 1.3 (Transport Security)
    • 5. CI/CD Security Scanning
    • Protected API Endpoints
    • Alertmanager Authentication
    • Environment Variables
    • Security Recommendations for Production
  • Data Sources
    • Dataset Summary
    • IMC 2025 Training Dataset
    • IMC 2025 Test Dataset
    • Data Versioning
    • Preprocessing Assumptions
    • Known Biases and Limitations

Project Context

  • Business Understanding
    • Problem Statement
    • Use Cases
    • ML Metric
    • Business and Operational Metrics
    • Data Source
    • Stakeholders
    • Definition of Done
  • Frequently Asked Questions
    • General
    • Installation & Setup
    • Using the API
    • Reconstruction Quality
    • MLOps / DVC / MLflow
    • Monitoring & Alerts
    • Data & Drift
MLOps 3D Scene Reconstruction
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