PongVerse
AI-Powered Multi-Camera Sports Analytics & 3D Reconstruction
An in-development Computer Vision research project exploring a unified 3D sports environment from synchronized multi-camera video streams. The work investigates detection, tracking, and spatial reconstruction.
The Challenge
The Problem with
Traditional Sports Analysis
Conventional single-camera sports analysis provides useful visual information, but has inherent limitations when recovering complete metric 3D and spatial information from a single viewpoint.
Limited depth information
A single 2D view does not directly provide metric depth, making accurate 3D distance estimation more challenging.
Occlusion blind spots
Players and equipment can occlude important scene information when observed from a single viewpoint.
Restricted viewing angles
A single camera observes the playing environment from only one viewpoint, limiting complete spatial coverage.
Limited 3D spatial understanding
Recovering reliable metric 3D positions from monocular video typically requires additional assumptions, learned priors, or scene constraints.
Limited biomechanical analysis
Single-view video can support useful analysis, but detailed 3D motion and biomechanical measurements are more difficult without additional viewpoints or constraints.
Our Solution
One Unified 3D View
from Many Cameras
PongVerse combines synchronized multi-camera vision, computer vision, and geometric reconstruction to build a unified 3D representation of the playing environment.
- 01
Multi-Camera Capture
Synchronized video streams from strategically positioned cameras covering the full playing environment.
- 02
AI Perception
Detection and pose-estimation models extract players, body keypoints, and ball observations from synchronized camera views.
- 03
Unified 3D Reconstruction
Camera calibration and multi-view geometry combine observations across cameras to recover positions in a shared 3D coordinate system.
Research Innovation
Inspired by Human
Binocular Vision
Human binocular vision demonstrates how observations from different viewpoints can provide depth information. PongVerse draws inspiration from this multi-view principle, using synchronized cameras, calibrated geometry, and computer vision to recover spatial information about the scene.
Biological Inspiration
Two eyes → brain fusion → depth perception
PongVerse Implementation
Multiple cameras → calibrated multi-view fusion → unified 3D representation
Engineering
The AI Pipeline
The research pipeline spans seven stages, from multi-camera capture and perception to 3D reconstruction and Unity visualization. The recorded demo and outputs show the current work.
- 01
Multi-Camera Capture
Simultaneous video acquisition from multiple synchronized cameras positioned around the table.
USB CamerasOBSFFmpeg - 02
Synchronization
Frame-level temporal alignment across all camera streams using timestamp correlation.
PythonNumPyOpenCV - 03
Player Detection
Real-time multi-class object detection identifies players and ball in every frame.
YOLOv8PyTorchCUDA - 04
Pose Estimation
Full-body 2D keypoint extraction per player per frame across all camera views.
AlphaPosePyTorchCOCO - 05
Camera Calibration
Intrinsic and extrinsic parameter estimation using ArUco marker-based calibration.
OpenCVArUcoNumPy - 06
3D Reconstruction
Triangulation and stereo geometry fuse multi-view 2D data into a unified 3D world model.
OpenCVSciPyNumPy - 07
Unity Visualization
Real-time 3D scene rendering with player avatars, ball trajectory, and spatial overlays.
UnityC#WebSocket
Output
Project Results
Visual outputs from each stage of the PongVerse pipeline. Click any frame to enlarge.
Demo
Recorded Research Demo
A recorded walkthrough of the PongVerse research pipeline, covering multi-camera capture, detection, pose estimation, calibration, reconstruction experiments, and Unity visualization.
What's Next?
Discuss a Custom
AI Workflow
PongVerse is part of Agentix Labs applied research. For custom AI systems and business automation, explore our solutions or discuss the workflow you want to improve.