arXiv:2606. 26465v1 Announce Type: cross Abstract: Spatial pattern matching is the process of matching query entities and constraints with database entities and relations.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The paper introduces COL‑Tree, a compact landmark‑based tree structure designed to accelerate hierarchical object search in spatial networks. It supports diverse query types—such as aggregate k‑nearest neighbor (AkNN), k‑farthest neighbor (kFN), and others—by enabling efficient graph traversal with a more accurate heuristic than Euclidean‑based methods. Experiments on real‑world and synthetic road, terrain, and physics mesh networks show that COL‑Tree outperforms existing approaches by up to three orders of magnitude, with only modest preprocessing overhead.
There have been many recent improvements in the ability of Large Language Models (LLMs) to perform complex tasks and answer domain-specific questions through techniques like Retrieval Augmented Generation (RAG). However, reasoning abilities of LLMs, including spatial reasoning abilities, are still lacking.
CitySTAR introduces a training‑free framework that transforms billion‑scale urban point clouds into a query‑ready scene graph of open‑vocabulary 3D instances, using CodeLLM‑driven tools to supply multimodal evidence for node attributes and spatial relations. It models target‑context topology with paired hypergraphs and performs bidirectional topology verification for structural disambiguation, followed by a Reflective Cross‑modal Grounding module that integrates topology consistency and 2D visual evidence to decide over a metric‑aware 3D context graph. The authors also present CitySTAR‑3D, a benchmark that enhances semantic coverage, instance completeness, bounding‑box fidelity, and spatial‑relation complexity for city‑scale 3D grounding, and report extensive experiments showing consistent improvements in open‑world urban 3D grounding with strong interpretability and generalization.
arXiv:2606. 03609v1 Announce Type: cross Abstract: Embodied agents that navigate cities rely on world models that predict how their surroundings will change as they move.
arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.
The paper introduces a sparse topometric graph for indoor navigation that encodes local 3D geometry in nodes and distance‑weighted edges for traversable connectivity. A learned graph‑matching network aligns subgraph structures across different sessions, enabling robust point‑goal navigation even with scene changes, sensor variations, or limited overlap. Experiments on Gibson and 3D‑FRONT datasets show higher success rates and a memory reduction of over two orders of magnitude compared to point‑level maps.