SIH26158Software
Single-Pass Drone Video to Accurate 3D Model Generation System
National Technical Research Organisation (NTRO)
Official Description
• Background:
Generation of accurate 3D models of buildings, infrastructure, terrain, and objects typically requires multiple drone passes, extensive image overlap, specialized flight planning, and significant post-processing time. In operational scenarios such as disaster response, surveillance, infrastructure inspection, military reconnaissance, and rapid mapping, there is often only a single opportunity to capture data over the target area. A solution capable of generating an accurate and textured 3D model from a single drone pass video would significantly reduce mission time, operator effort, data acquisition requirements, and processing complexity while enabling near real-time situational awareness.
• Description:
Design and develop an AI-enabled system capable of generating a georeferenced and metrically accurate 3D model of a scene using only a single-pass drone video stream captured from a moving UAV. The system should process video frames captured during one flight path and reconstruct:
(i) 3D terrain and structures (ii) Building facades and rooftops (iii) Roads and infrastructure (iv) Vegetation and obstacles (v) Textured 3D meshes or point clouds
• Expected Solution/Deliverables:
The generated model should be suitable for visualization, measurement, and analysis purposes.
• Key Challenges (i) Limited viewing angles due to single flight path.
(ii) Motion blur and video compression artifacts.
(iii) Variable illumination and shadows.
(iv) Dynamic objects (vehicles,humans, animals).
(v) GPS inaccuracies and sensor noise.
(vi) Real-time or near-real-time processing requirements.
(vii) Reconstruction of occluded surfaces.
(viii) Maintaining metric accuracy without extensive Ground Control Points (GCPs).
• Input Data :
• Mandatory (i) Drone video (1080p/4K)
(ii) GPS coordinates (iii) Flight metadata
• Optional (i) IMU data (ii) Barometric altitude (iii) Camera intrinsic parameters (iv) RTK/PPK corrections Add 'Desired Output' and 'Evaluation Criteria' table here
• Potential Applications :
(i) Border and strategic area mapping (ii) Disaster damage assessment (iii) Urban planning and smart cities (iv) Infrastructure inspection (v) Construction progress monitoring (vi) Archaeological documentation (vii) Digital twin generation (viii) Military reconnaissance and mission planning
Official Hackathon Facts
Theme / DomainRobotics and Drones
Track TrackSoftware
Submission Deadline20 September 2026
Ideas Registered0/500
Source DatasetExternal Resource ↗
✨ SIH Fit Analysis
Estimated DifficultyAdvanced
Social Impact Score (5/5)
🔥 High Impact🌍 High Social Impact🛡️ Strategic / Security🚀 Advanced Technology
Primary Tech AreaGenerative AI
Technology Stack Tags
AI/MLComputer VisionGenerative AISensorsRoboticsDroneAerial MappingMobile DevelopmentSimulationDigital Twin
Suggested Skills
PythonOpenCVSensor interfacingROS / Robotics controlFlight controllers (PX4/ArduPilot)Flutter / React NativeSimulation tools (Unity/MATLAB)Digital Twin platforms
Notable Technology Combo
Computer Vision + Drone / UAVRobotics + AIRobotics + Computer VisionDrone + AI/ML
✦ Why This Problem is Interesting
Combines Computer Vision, Robotics, Drone with the problem domain, creating a technically focused solution opportunity.
* Note: SIH Fit Analysis contains derived ratings and classification schemas generated to aid team selection; these are not official ratings from the Smart India Hackathon organizers.
