Abstract: We propose Graph2Nav, a real-time 3D object-relation graph generation framework, for autonomous navigation in the real world. Our framework fully generates and exploits both 3D objects and a ...

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, ...

@misc{bavle2025sgraphs20hierarchicalsemantic, title={S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM}, author={Hriday Bavle and Jose ...

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.

The authors describe an interesting approach to studying the dynamics and function of membrane proteins in different lipid environments. The important findings have theoretical and practical ...

An inherent principle of publication is that others should be able to replicate and build upon the authors' published claims. A condition of publication in a Nature Portfolio journal is that authors ...