Orchestrating Spatial Semantics via a Zone-Graph Paradigm for Intricate Indoor Scene Generation
- URL: http://arxiv.org/abs/2605.02537v1
- Date: Mon, 04 May 2026 12:40:37 GMT
- Title: Orchestrating Spatial Semantics via a Zone-Graph Paradigm for Intricate Indoor Scene Generation
- Abstract summary: We present ZoneMaestro, a unified framework for autonomous 3D indoor synthesis.<n>By internalizing a novel topological-based logic, ZoneMaestro translates high-level semantic intent into functional zones and constraints.<n>We show that ZoneMaestro resolves the density-safety dichotomy significantly outperforming state-of-the-art baselines in both structural coherence and intent adherence.
- Score: 18.745553312257503
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autonomous 3D indoor scene synthesis breaks down in non-convex rooms with tightly coupled spatial constraints. Data-driven generators lack topological priors for long-horizon planning, while iterative agents fragment semantics and become geometrically brittle. We present ZoneMaestro, a unified framework that shifts the paradigm from object-centric synthesis to Zone-Graph Orchestration. By internalizing a novel zone-based logic, ZoneMaestro translates high-level semantic intent into functional zones and topological constraints, enabling robust adaptation to diverse architectural forms. To support this, we construct Zone-Scene-10K, a large-scale dataset enriched with explicit Zone-Graph annotations. We further introduce an Alternating Alignment Strategy that cycles between reasoning internalization and Zone-Aware Group Relative Policy Optimization (Z-GRPO), effectively reconciling the tension between semantic richness and geometric validity without relying on external physics engines. To rigorously evaluate spatial intelligence beyond convex primitives, we formally define the task of Intricate Spatial Orchestration and release SCALE, a stress-test benchmark for irregular indoor scenarios with complex, dense spatial relations. Extensive experiments demonstrate that ZoneMaestro resolves the density-safety dichotomy, significantly outperforming state-of-the-art baselines in both structural coherence and intent adherence.
Related papers
- Prior-SG: Task and Prior Driven Region Segmentation for Scene Graphs in Arbitrarily-Structured Environments [20.027960785504586]
We propose Prior-SG, a task- and prior-driven framework that casts scene graph generation as a probabilistic alignment problem.<n>We validate this approach across diverse simulated residential datasets and large, open-plan real-world environments.
arXiv Detail & Related papers (2026-08-06T15:36:12Z) - DeSeG: Decoupling Semantic Intent and Geometric Constraints for Physically Plausible Human-Scene Interaction [50.06148080975928]
We propose DeSeG, a hierarchical framework that explicitly decouples semantic intent from geometric constraints.<n>DeSeG achieves state-of-the-art performance, reducing mean scene penetration by 47% and improving semantic alignment by 29% over the SOTA baselines.
arXiv Detail & Related papers (2026-07-07T03:21:08Z) - DeGenseGS: Geometrically and Semantically Decoupled Surgical Scene Understanding in 4D Gaussian Splatting [17.162560675768642]
Existing solutions integrate Vision-Language Models into deformable fields via a rigid coupling scheme that tightly binds semantic features to geometric warping.<n>We propose DeGenseGS, Geometric Understandingally and Semantically Decoupled Scene in 4D Splatting, a novel framework that independently models semantic evolution and geometric deformation.<n>Our framework yields enhanced geometric robustness and robust semantic-anatomic alignment, enabling spatially continuous segmentation despite drastic tissue deformation and topological transitions.
arXiv Detail & Related papers (2026-07-06T07:56:17Z) - SpaceEra++: A Unified Framework Towards 3D Spatial Reasoning in Video [88.79625979053873]
We propose a novel framework, SpaceEra, for pre-trained vision-language models (VLMs)<n>We extend the original framework into a comprehensive system, termed SpaceEra++, which spans data construction, model design, training optimization, and prompting inference.<n>In addition, to enhance spatial reasoning, we develop SpaceAlign, which enforces pairwise object constraints by jointly exploiting absolute coordinates and relative spatial relations.
arXiv Detail & Related papers (2026-07-02T06:56:29Z) - Curvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understanding [26.656996180165795]
Existing approaches that apply Euclidean embedding spaces struggle to simultaneously preserve fine-grained local geometric details.<n>We propose a novel textbftextscCurvature-Aware Captioning framework, integrating novel non-Euclidean geodesic attention mechanisms.
arXiv Detail & Related papers (2026-05-09T08:54:42Z) - Riemannian and Symplectic Geometry for Hierarchical Text-Driven Place Recognition [6.392844932864485]
SympLoc is a novel coarse-to-fine localization framework with multi-level alignment in the coarse stage.<n>It achieves a 19% improvement in Top-1 recall@10m compared to existing state-of-the-art approaches.
arXiv Detail & Related papers (2026-04-02T04:13:42Z) - LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting [77.81227097905865]
Anchor-based 3DGS compression schemes reduce gaussian redundancy through some advanced context models.<n>We propose a Local Geometry-aware Hierarchical Context Compression framework for 3DGS that incorporates inter-anchor geometric correlations into anchor pruning and entropy coding for compact representation.<n>Experiments show that LG-HCC effectively alleviates structural preservation issues,achieving superior geometric integrity and rendering fidelity while reducing storage by up to 30.85x compared to the Scaffold-GS baseline on the Mip-NeRF360 dataset.
arXiv Detail & Related papers (2026-03-30T13:39:35Z) - Thinking with Geometry: Active Geometry Integration for Spatial Reasoning [68.59084007360615]
We propose GeoThinker, a framework that shifts paradigm passive fusion to active perception.<n>Instead of feature mixing, GeoThinker enables the model to selectively retrieve geometric evidence conditioned on its internal reasoning demands.<n>Our results indicate that the ability to actively integrate spatial structures is essential for next-generation spatial intelligence.
arXiv Detail & Related papers (2026-02-05T18:59:32Z) - RSGround-R1: Rethinking Remote Sensing Visual Grounding through Spatial Reasoning [61.84363374647606]
Remote Sensing Visual Grounding (RSVG) aims to localize target objects in large-scale aerial imagery based on natural language descriptions.<n>These descriptions often rely heavily on positional cues, posing unique challenges for Multimodal Large Language Models (MLLMs) in spatial reasoning.<n>We propose a reasoning-guided, position-aware post-training framework, dubbed textbfRSGround-R1, to progressively enhance spatial understanding.
arXiv Detail & Related papers (2026-01-29T12:35:57Z) - GFLAN: Generative Functional Layouts [1.1458853556386797]
GFLAN is a generative framework that restructures floor plan synthesis through explicit factorization into topological planning and geometric realization.<n>Our approach departs from direct pixel-to-pixel or wall-tracing generation in favor of a principled two-stage decomposition.
arXiv Detail & Related papers (2025-12-18T07:52:47Z) - GeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs [59.61242815508687]
Graph neural networks (GNNs) on text--attributed graphs (TAGs) encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear neighborhood aggregation.<n>This work introduces a local PCA-based metric that measures the degree of semantic drift and provides the first quantitative framework to analyze how different aggregation mechanisms affect manifold structure.
arXiv Detail & Related papers (2025-11-12T06:48:43Z) - HierRelTriple: Guiding Indoor Layout Generation with Hierarchical Relationship Triplet Losses [52.70183252341687]
We present a hierarchical triplet-based indoor relationship learning method, coined HierRelTriple, with a focus on spatial relationship learning.<n>We introduce HierRelTriple, a hierarchical relational triplets modeling framework that first partitions functional regions and then automatically extracts three levels of spatial relationships.<n>Experiments on unconditional layout synthesis, floorplan-conditioned layout generation, and scene rearrangement demonstrate that HierRel improves spatial-relation metrics by over 15%.
arXiv Detail & Related papers (2025-03-26T07:31:52Z) - EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks [24.41705039390567]
EmbodiedVSR (Embodied Visual Spatial Reasoning) is a novel framework that integrates dynamic scene graph-guided Chain-of-Thought (CoT) reasoning.<n>Our method enables zero-shot spatial reasoning without task-specific fine-tuning.<n>Experiments demonstrate that our framework significantly outperforms existing MLLM-based methods in accuracy and reasoning coherence.
arXiv Detail & Related papers (2025-03-14T05:06:07Z) - Achieving Hyperbolic-Like Expressiveness with Arbitrary Euclidean Regions: A New Approach to Hierarchical Embeddings [9.614222676567385]
We present RegD, a flexible Euclidean framework that supports the use of arbitrary geometric regions as embedding representations.<n>RegD achieves hyperbolic-like expressiveness by incorporating a depth-based dissimilarity between regions, enabling it to emulate key properties of hyperbolic geometry.<n>Our empirical evaluation on diverse real-world datasets shows consistent performance gains over state-of-the-art methods.
arXiv Detail & Related papers (2025-01-29T09:44:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.