VERSES AI Research Blog – “In recent years, computer vision has made remarkable progress in understanding 3D scenes, fueled by innovations like Neural Radiance Fields (NeRF) and Gaussian Splatting. These methods are the backbone of applications, ranging from virtual reality to robotics, where creating realistic 3D models from simple 2D images is key. However, one major challenge remains unsolved: how can systems continually learn from new data without forgetting what they already know?

Our new method, Variational Bayes Gaussian Splatting (VBGS), offers a promising solution. Designed with continual learning in mind, VBGS enables machines to integrate new data efficiently and adapt to dynamic environments without needing massive computational resources or memory. Let’s explore what makes VBGS special and why it’s a leap forward for 3D scene understanding.”