Chamfer Distance

Chamfer distance is a metric used to measure the similarity between two point clouds, finding applications in diverse fields like 3D reconstruction and point cloud completion. Current research focuses on improving the Chamfer distance's effectiveness by addressing limitations such as asymmetry, sensitivity to outliers, and insensitivity to local density variations, leading to the development of weighted, differentiable, and density-aware variants. These advancements enable more accurate and robust 3D shape representation and analysis, impacting fields ranging from robotics (e.g., scene understanding) to computer graphics (e.g., mesh reconstruction).