mlsanityData to 7 Oct 2026

Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild

arXiv 2403.14539 ↗First posted 21 Mar 2024cs.CV cs.AI cs.LGcited by 7

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Abstract (arXiv)

Recent monocular 3D shape reconstruction methods have shown promising zero-shot results on object-segmented images without any occlusions. However, their effectiveness is significantly compromised in real-world conditions, due to imperfect object segmentation by off-the-shelf models and the prevalence of occlusions. To effectively address these issues, we propose a unified regression model that integrates segmentation and reconstruction, specifically designed for occlusion-aware 3D shape reconstruction. To facilitate its reconstruction in the wild, we also introduce a scalable data synthesis pipeline that simulates a wide range of variations in objects, occluders, and backgrounds. Training on our synthetic data enables the proposed model to achieve state-of-the-art zero-shot results on real-world images, using significantly fewer parameters than competing approaches.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 86 of its 104 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

86 of 104 references.

References · 86

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

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  1. 2024Part123: Part-aware 3D Reconstruction from a Single-view Image
  2. 2026ShapeR: Robust Conditional 3D Shape Generation from Casual Captures
  3. 2024Zero-Shot Multi-Object Scene Completion
  4. 2025SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images
  5. 2023Anything-3D: Towards Single-view Anything Reconstruction in the Wild
  6. 2025ZeroScene: A Zero-Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editing
  7. 2023Zero-Shot 3D Shape Correspondence
  8. 2025Zero-P-to-3: Zero-Shot Partial-View Images to 3D Object