Revolutionizing 3D Object Tracking with Dynamic 3D Gaussians

Making 3D Object Tracking Simple with Dynamic 3D Gaussians

Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis

4 min readOct 31, 2023



In the rapidly evolving field of computer vision, tracking objects in dynamic 3D environments is crucial for applications ranging from autonomous driving to robotic navigation. A novel method, Dynamic 3D Gaussians, has emerged as a promising solution, leveraging the power of dynamic view synthesis. This article delves deep into the intricacies of this method, offering insights, code snippets, and best practices to empower readers to harness this technology for their projects.

Why Dynamic 3D Gaussians?

The necessity for dynamic 3D object tracking is evident in many modern applications. Traditional methods often falter in complex, dynamic scenarios. Dynamic 3D Gaussians offers:

  • Accurate Tracking: Improved accuracy over static models, especially in cluttered environments.
  • Efficiency: Optimized for real-time applications.
  • Versatility: Adaptable to various scenarios, from gaming to industrial applications.
# Placeholder for initialization code
# import necessary libraries
import dynamic3dgaussians as d3g

Persistent Dynamic View Synthesis

At the core of this method is the concept of view synthesis. By constantly updating the synthesized view, the system can adjust to changes in the environment and maintain accurate tracking.

# Example of updating the synthesized view
def update_synthesized_view(data):
# Process data
# ...
updated_view = d3g.synthesize_view(data)
return updated_view

Implementing the Core Algorithm

To harness the power of Dynamic 3D Gaussians, one needs to understand its core algorithm:



Javier Calderon Jr

CTO, Tech Entrepreneur, Mad Scientist, that has a passion to Innovate Solutions that specializes in Web3, Artificial Intelligence, and Cyber Security