KNA-SG: Keyframe–Node-Associated Open-Vocabulary 3D Scene Graphs from RGB Sequences

Beyond Isolated Objects: Relationship-aware Open Vocabulary Scene Understanding via 3D Scene Graph Analysis

Open-vocabulary 3D scene understanding aims to segment 3D scenes beyond predefined categories by transferring semantic knowledge from vision-language models. Existing methods have advanced this task by lifting language-aligned 2D features into 3D, yet they often rely on context-independent semantic representations, leaving object relationships underexplored for contextual refinement. We propose RelGraphOV, a relationship-aware framework that uses 3D scene graphs to enhance open-vocabulary 3D understanding. Our method constructs relational scene graphs from multi-view observations by leveraging vision-language reasoning to infer object relationships and prune geometrically implausible connections, without manual relationship annotations. To aggregate relational context while avoiding feature interference, we introduce an Adaptive Gated Dual-Stream Contextual GAT that separates dense geometric features and semantic CLIP embeddings, performs edge-guided message passing, and adaptively fuses complementary semantics. A hierarchical contrastive objective further promotes instance-level consistency and category-level discrimination. Experiments on ScanNetV2, ScanNet200, ScanNet$++$, and Replica demonstrate strong performance and generalization ability. Project Page: https://cxavireh.github.io/relgraphov-projectpage

OVIP-SG: Open-Vocabulary Instance-Preserving Scene Graphs for Mapping and Retrieval of Small, Fine-Grained Objects

OVIP-SG is presented, a unified framework for instance-preserving semantic mapping, functional scene partitioning, and language-guided small, fine-grained object retrieval that outperforms ConceptGraphs under a unified evaluation protocol on Replica.

SPOT: Structured Prompting with Object-centric Tokens for open-world scene graphs

Struct-Align: Zero-Shot Text-to-3D Scene Retrieval via Locality-Aware Structural Alignment

Text-to-3D Scene Retrieval (T3SR) aims to retrieve 3D scenes that match users' linguistic queries, enabling intuitive access to 3D scene repositories. Existing approaches rely on joint embedding learning with large amounts of paired text–scene data, which is expensive to collect and often fails to generalize under open-vocabulary queries and diverse scene distributions. In this paper, we propose Struct-Align, a foundation-model-driven framework for zero-shot T3SR that eliminates the need for paired training data. Our key insight is to reformulate T3SR as a single-modality structural alignment problem by converting both 3D scenes and textual queries into a shared, schema-aligned textual representation compatible with pretrained text embedding models. To reliably derive such representations from complex 3D environments, we introduce a role-decomposed scene structuring pipeline that mitigates generative instability and produces semantically consistent scene depictions. To address the inherent semantic asymmetry between query and scene representations, we further propose a locality-aware structural matching strategy that explicitly localizes query intent and performs instance- and relation-level alignment within query-relevant substructures. Extensive experiments on multiple benchmarks demonstrate that Struct-Align outperforms both training-based and zero-shot baselines while exhibiting strong robustness to domain shift.

GroupForward: Building Referable 3D Scenes via Instance-Grouped Feed-Forward Gaussian Splatting

This work proposes GroupForward, an instance-grouped feed-forward Gaussian splatting model that reconstructs geometry, appearance, instance structure, and semantics from sparse, unposed, and uncalibrated multi-view images and proposes a Referential Scene Reasoning Framework (RSRF) for complex 3D referring segmentation.

Interactive Open-Set Semantic Mapping with a 3D Scene Graph Backend

A modular mapping architecture is demonstrated that establishes 3D Semantic Scene Graphs (3DSSGs) as its foundational back-end, enabling the dense representation of extensive environments containing thousands of unique object instances and supporting open-vocabulary queries via CLIP features without requiring any additional post-processing steps.