Abstract: Cross-modal 3D shape retrieval is a crucial and widely applied task in the field of 3D vision. Its goal is to construct retrieval representations capable of measuring the similarity between ...

Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Erika Rasure is globally-recognized as a ...

Many layers: knife sharpness and cutting technique greatly affect the speed of droplets that are created when cutting an onion (courtesy: Zixuan Wu and Sunghwan “Sunny” Jung at Cornell University) ...

In this paper, we present VoxT-GNN, an innovative framework that harnesses the strengths of both Transformer and Graph Neural Network architectures for 3D object detection from LiDAR point clouds.

Notifications You must be signed in to change notification settings Our system solves advanced mathematical problems with agentic workflow powered by LLM APIs and mathematical tools. At the beginning, ...