Mesh Map

Mesh maps represent a dense, continuous 3D representation increasingly used in robotics, computer vision, and fluid dynamics, offering advantages over point clouds for tasks requiring detailed geometric information. Current research focuses on developing efficient mesh construction and manipulation methods, often leveraging graph neural networks (GNNs) for tasks like super-resolution and adaptive mesh refinement, and incorporating dynamic object removal for robust mapping in real-world scenarios. These advancements improve the accuracy and efficiency of various applications, including simultaneous localization and mapping (SLAM), high-fidelity 3D modeling, and the solution of partial differential equations. The resulting improvements in accuracy and efficiency have significant implications for fields like autonomous navigation, virtual/augmented reality, and scientific computing.