ARXIV:2601.21786 · 3D RECONSTRUCTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Synthetic-to-Real Domain Bridging for Single-View 3D Reconstruction of Ships for Maritime Monitoring

arXiv

AI-powered single-view 3D reconstruction for real-time maritime ship monitoring using synthetic-to-real domain bridging.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain AI-powered single-view 3D reconstruction for real-time maritime ship monitoring using synthetic-to-real domain bridging.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

AI-powered single-view 3D reconstruction for real-time maritime ship monitoring using synthetic-to-real domain bridging. However, most state-ofthe-art 3D reconstruction methods require multi-view supervision, annotated 3D ground truth, or are computationally intensive, making them impractical for…

METHOD

Full abstract

Three-dimensional (3D) reconstruction of ships is an important part of maritime monitoring, allowing improved visualization, inspection, and decision-making in real-world monitoring environments. However, most state-ofthe-art 3D reconstruction methods require multi-view supervision, annotated 3D ground truth, or are computationally intensive, making them impractical for real-time maritime deployment. In this work, we present an efficient pipeline for single-view 3D reconstruction of real ships by training entirely on synthetic data and requiring only a single view at inference. Our approach uses the Splatter Image network, which represents objects as sparse sets of 3D Gaussians for rapid and accurate reconstruction from single images. The model is first fine-tuned on synthetic ShapeNet vessels and further refined with a diverse custom dataset of 3D ships, bridging the domain gap between synthetic and real-world imagery. We integrate a state-of-the-art segmentation module based on YOLOv8 and custom preprocessing to ensure compatibility with the reconstruction network. Postprocessing steps include real-world scaling, centering, and orientation alignment, followed by georeferenced placement on an interactive web map using AIS metadata and homography-based mapping. Quantitative evaluation on synthetic validation data demonstrates strong reconstruction fidelity, while qualitative results on real maritime images from the ShipSG dataset confirm the potential for transfer to operational maritime settings. The final system provides interactive 3D inspection of real ships without requiring real-world 3D annotations. This pipeline provides an efficient, scalable solution for maritime monitoring and highlights a path toward real-time 3D ship visualization in practical applications. Interactive demo: https://dlr-mi.github.io/ship3d-demo/.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Quantitative evaluation on synthetic validation data demonstrates strong reconstruction fidelity, while qualitative results on real maritime images from the ShipSG dataset confirm the potential…

WHY NOW

3D Reconstruction moved forward this cycle; last verified April 2026. Public score 7.0/10.

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Opportunity summary

Score7.0

PainAI-powered single-view 3D reconstruction for real-time maritime ship monitoring using synthetic-to-real domain bridging.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

AI-powered single-view 3D reconstruction for real-time maritime ship monitoring using synthetic-to-real domain bridging.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

AI-powered single-view 3D reconstruction for real-time maritime ship monitoring using synthetic-to-real domain bridging.

Segment

3D Reconstruction

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

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Competitive landscape

AI-powered single-view 3D reconstruction for real-time maritime ship monitoring using synthetic-to-real domain bridging.

Segment

3D Reconstruction

Adoption evidence

No public code link in the paper record yet

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7.0/10 public viability

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not classified

Adjacent

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Substitute

not classified

Unknown

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Synthetic-to-Real Domain Bridging for Single-View 3D Reconstruction of Ships for Maritime Monitoring

Synthetic-to-Real Domain Bridging for Single-View 3D Reconstruction of Ships for Maritime Monitoring

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