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ARXIV:2604.02583 · MULTIMODAL RETRIEVAL · SUBMITTED 06 APR · 20:15 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02583MULTIMODAL RETRIEVALSUBMITTED 06 APR · 20:15 UTCFRESHNESS UNKNOWNWei Li · Yufan Ren · Hanqing Jiang · Jianhui Ding · Zhen Peng · Leman Feng · +3 at arXiv
FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding.
Opportunity summary
Pain FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding.
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Blocker Evidence unverified
FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its…
We propose FusionBERT, a novel multi-view visual fusion framework for image-3D multimodal retrieval. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting their…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive image-3D retrieval experiments demonstrate that FusionBERT achieves significantly higher retrieval accuracy than SOTA multimodal large models under both single-view and multi-view settings, establishing…
Multimodal Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding.
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10.48550/arXiv.2604.02583FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding.
Abstract
We propose FusionBERT, a novel multi-view visual fusion framework for image-3D multimodal retrieval. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting their applicability in realistic scenarios where an object is typically observed and captured from multiple viewpoints. Although multi-view observations naturally provide complementary geometric and appearance cues, existing multimodal large models rarely explore how to effectively fuse such multi-view visual information for better cross-modal retrieval. To address this limitation, we introduce a multi-view image-3D retrieval framework named FusionBERT, which innovatively utilizes a cross-attention-based multi-view visual aggregator to adaptively integrate features from multi-view images of an object. The proposed multi-view visual encoder fuses inter-view complementary relationships and selectively emphasizes informative visual cues across multiple views to get a more robustly fused visual feature for better 3D model matching. Furthermore, FusionBERT proposes a normal-aware 3D model encoder that can further enhance the 3D geometric feature of an object model by jointly encoding point normals and 3D positions, enabling a more robust representation learning for textureless or color-degraded 3D models. Extensive image-3D retrieval experiments demonstrate that FusionBERT achieves significantly higher retrieval accuracy than SOTA multimodal large models under both single-view and multi-view settings, establishing a strong baseline for multi-view multimodal retrieval.
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PROBLEM
FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting th...
METHOD
We propose FusionBERT, a novel multi-view visual fusion framework for image-3D multimodal retrieval. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting their applicability in realistic sc...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive image-3D retrieval experiments demonstrate that FusionBERT achieves significantly higher retrieval accuracy than SOTA multimodal large models under both single-view and multi-view settings, esta...
WHY NOW
Multimodal Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting their applicability in realistic scenarios where an object is typically observed and captured from multiple viewpoints.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We propose FusionBERT, a novel multi-view visual fusion framework for image-3D multimodal retrieval. Existing image-3D representation learning methods predominantly focus on feature alignment of a single object image and its 3D model, limiting their applicability in realistic scenarios where an object is typically observed and captured from multiple viewpoints.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive image-3D retrieval experiments demonstrate that FusionBERT achieves significantly higher retrieval accuracy than SOTA multimodal large models under both single-view and multi-view settings, establishing a strong baseline for multi-view multimodal retrieval. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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FusionBERT enables robust multi-view image-to-3D model retrieval by adaptively fusing visual cues and enhancing 3D geometry encoding.
Segment
Multimodal Retrieval
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