November 1st, 2024 – 11am-12pm, EC4-2101A

***11:00 am Presenter: Yan Song (Kevin) Hu***

As robotics and AI systems become more sophisticated, the demand for real-time, high-quality spatial data has increased in importance. A promising approach for generating such data comes from emerging radiance field techniques, particularly 3D Gaussian Splatting, that can quickly generate dense maps of environments. My research focuses on generating dense volumetric maps in real-time by combining 3D Gaussian Splatting with Direct Sparse Odometry (DSO), a state-of-the-art robotic navigation system. I have found that certain characteristics of DSO, especially its pixel-based tracking method, enable 3D Gaussian Splatting to produce maps more quickly and with greater quality.

***11:30 am Presenter: Yizie Liu***

We introduce a hybrid approach to solve two popular industrial tasks: Image-based Scene Change Detection (SCD) and Pose-agnostic Object Anomaly Detection (PAD). Our method maintains both a learning-based model Gaussian Splatting for Novel View Synthesis and a Structure-from-Motion model (Hierarchical Localization) for localization, which takes the advantage of the fast training and inference of 3D Gaussian Splatting and the fast localization of Hierarchical Localization. We also explored the possibility of SAM2 Based Mask Refinement on the SCD task, where the change is object-level.