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Research map / 3D vision / 3D generation

What Do Single-View 3D Reconstruction Networks Learn?

CVPR 2019 · 3D vision › 3D generation · approach: Learning 3D reconstruction from images · 455 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use image-based shape retrieval instead of encoder-decoder reconstructionSingle-view 3D reconstruction on ShapeNet · ShapeNet (55 classes)mIoU: 0.47 -> 0.57Helped
Use image classification to choose a cluster mean shape instead of encoder-decoder reconstructionSingle-view 3D reconstruction on ShapeNet · ShapeNet (55 classes)mIoU: 0.38 -> 0.46; 0.46 -> 0.46; 0.47 -> 0.46Mixed
Use the Oracle nearest training shape as the predictionShapeNet single-view reconstruction · ShapeNet (55 classes)mIoU: 0.57 -> 0.76Helped
Evaluate in a viewer-centered rather than object-centered coordinate frameOGN and Matryoshka Networks · ShapeNetmIoUHurt
Evaluate retrieval in a viewer-centered rather than object-centered coordinate frameRetrieval · ShapeNetmIoUHurt
Use viewer-centered reconstruction and assess surface accuracy with F-score at a 1% thresholdRetrieval · ShapeNet (viewer-centered)F-score (@1%)Mixed
Use viewer-centered reconstruction and assess surface accuracy with F-score at a 1% thresholdAtlasNet · ShapeNet (viewer-centered)F-score (@1%)Mixed
Train and evaluate on ShapeNet's canonical object-centered, within-class shape splitSingle-view 3D reconstruction methods · ShapeNetmIoU: 0.47 -> 0.76Helped

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