TL;DR
SpecBridge is introduced, a 3D-2D-Text pre-training framework that leverages CLIP priors as a foundational bridge to connect three modalities by synergizing spectral graph theory with transitive semantic learning.
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.
High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typically requires learning geometry from scratch and overlooks the rich semantic and structural priors already encapsulated in discriminative 3D foundation models. We contend that leveraging the profound understanding of the 3D world possessed by these discriminative models can significantly reduce generative cost. To this end, we propose ROAD, a framework that reduces the training cost of 3D generation by transferring these rich discriminative priors into diffusion transformers. To address the inherent semantic-structural heterogeneity between generative and discriminative latents, we introduce a reciprocal-objective alignment strategy. This method synergizes Holistic Semantic Condensing to enforce global semantic coherence and Structural Optimal Alignment, which is formulated as a bipartite matching problem to rigorously align microscopic geometric details between disparate latent spaces. The 3D foundation model is only used for training-time supervision of alignment and is not used at inference, incurring no additional inference cost. Compared with the industrial baseline Step1X-3D, the proposed ROAD achieves highly competitive generation performance with only 1.5% of the training data and significantly reduces training costs, effectively reducing the computational overhead of high-fidelity 3D generation. Code is available at https://github.com/H-EmbodVis/ROAD.
Open-vocabulary semantic segmentation (OVSS) leverages textual semantics to segment objects beyond predefined categories. While the self-supervised model DINOv3 provides strong structured visual representations, its lack of native textual alignment hinders its direct application to OVSS. To bridge this gap, we propose DINOde, an ODE-based framework that continuously aligns CLIP text embeddings with the DINO visual manifold. Our approach employs two complementary components: (i) Semantic Text Flow (STF), which evolves text embeddings toward the DINO manifold through a continuous ODE trajectory, and (ii) Global Context Flow (GCF), which progressively refines the holistic image representation carried by DINO's CLS token. To preserve the hyperspherical geometry of the feature space during this evolution, we further introduce Velocity Tangent Projection, which constrains the learned velocity field to the tangent space. By modeling alignment as a continuous trajectory, DINOde avoids the manifold entanglement inherent in discrete MLP projections and yields more robust cross-modal alignment. Extensive experiments demonstrate that DINOde consistently outperforms existing methods and achieves state-of-the-art performance across multiple OVSS benchmarks. The code is available at https://github.com/yoon307/DINOde.
Unsupervised 2D image-based 3D model retrieval (IBMR) aims to retrieve semantically relevant 3D shapes for a given 2D image query when 3D annotations are unavailable. This setting is challenging due to severe modality gaps, category-imbalanced mini-batches, inconsistent cross-domain decision boundaries, and mismatched semantic neighborhood structures. In this paper, we propose a unified framework that integrates Category-Aligned Sampling (CAS), Decision Boundary Alignment (DBA), and Graph Semantic Propagation (GSP) into a single optimization paradigm. CAS constructs category-consistent mini-batches to stabilize crossmodal learning. Built upon CAS, DBA leverages a masked Margin Disparity Discrepancy to regularize cross-domain class decision boundaries via an adversarial min-max objective, encouraging discriminative separation beyond marginal feature matching. To complement boundary-level regularization, GSP builds a crossdomain affinity graph over 2D and 3D samples and propagates supervision-induced relational structure through semantic message passing, explicitly preserving instance-level neighborhood consistency that is critical for retrieval. Extensive experiments on MI3DOR and MI3DOR-2 demonstrate consistent improvements over representative unsupervised IBMR baselines.
Qwen-3D is introduced, a geometry-aware LMM that compresses visual information within the Qwen backbone using multi-view geometric cues, enabling efficient long-horizon visual reasoning over static scenes and incorporates a query-based segmentation decoder that grounds language directly in the underlying 3D scene representation.
Canonicalizing 3D object orientation is fundamental to 3D understanding and analysis. Existing approaches often rely on geometric cues, although 3D canonicalization ultimately requires a semantically meaningful orientation. To address this gap, we propose CANIS, a category-agnostic, generation-assisted framework that introduces the semantic orientation prior of a frozen image-to-3D generative model into 3D canonicalization, without canonicalization-specific training or category-specific templates. Specifically, CANIS first renders the input object from candidate viewpoints, selects an informative view, and generates a proxy in a canonical orientation. During generation, a sparse structural latent encoded from the input guides the proxy to preserve the geometry of an object. CANIS then uses the selected image as a semantic bridge between the input and the proxy. Image patches identify semantic regions on the proxy, and depth back-projection locates the corresponding regions on the input. The resulting semantic anchors constrain geometric matching, from which we estimate the rigid transformation that canonicalizes the input. Experiments on synthetic benchmarks validate CANIS and its key components, while qualitative results on partial observations and OmniObject3D suggest its applicability to incomplete and real-world scans. CANIS also improves downstream 3D classification, part segmentation, and dense correspondence under arbitrary rotations. Project page: https://kenkenzaii.github.io/Canis.
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