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Research map / 3D vision / Face and shape

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

NeurIPS 2019 · 3D vision › Face and shape · approach: Learned mesh and geometric representations · 254 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DIB-R instead of SoftR.Single-image 3D object prediction · ShapeNet 13 categories, mean3D IOU (%): 59.3 -> 61.2Helped
Use DIB-R instead of N3MRSingle-image 3D object prediction · ShapeNet 13 categories, mean3D IOU (%): 57.0 -> 61.2Helped
Use DIB-R instead of SoftR.Single-image 3D object prediction · ShapeNet 13 categories, meanF-score (%): 49.9 -> 55.8Helped
Use DIB-R instead of N3MRSingle-image 3D object prediction · ShapeNet 13 categories, meanF-score (%): 54.7 -> 55.8Helped
Use DIB-R instead of N3MRTexture and lighting prediction · ShapeNet carsTexture L-1 loss: 0.03640 -> 0.02179Helped
Use DIB-R instead of N3MRTexture and lighting prediction · ShapeNet carsLighting angle: 23.5585 -> 9.7096Helped
Use DIB-R instead of N3MRTexture and lighting prediction · ShapeNet carsTexture + light L-1 loss: 0.02208 -> 0.01362Helped
Use DIB-R instead of CMRSingle-view shape and texture prediction · CUB birds2D IOU loss: 0.262 -> 0.243Helped
Use DIB-R instead of CMRSingle-view shape and texture prediction · CUB birdsKey Point: 0.930 -> 0.972Helped
Use DIB-R instead of CMRSingle-view shape and texture prediction · CUB birdsTexture L-1 loss: 0.043 -> 0.043No clear effect
Add adversarial lossDIB-R texture and lighting prediction · ShapeNet carsTexture crispnessHelped
Predict shininess from 2D supervisionDIB-R Phong lighting model · ShapeNet carsShininess prediction accuracyHurt

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