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, ...
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 ...
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning. Based on the substantial ...
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