Gaussian Splat

Gaussian splatting is a novel 3D scene representation technique that models objects and scenes as collections of Gaussian distributions, enabling efficient rendering and manipulation. Current research focuses on improving the accuracy and efficiency of Gaussian splatting for various applications, including real-time LiDAR simulation, novel view synthesis, and dynamic scene reconstruction, often incorporating techniques like neural fields and variational inference to enhance performance and address limitations such as splat drift and catastrophic forgetting. This approach offers significant advantages in speed and scalability compared to traditional methods, impacting fields like robotics, autonomous driving, and virtual/augmented reality through improved scene representation and real-time rendering capabilities.