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DSAC - Differentiable RANSAC for Camera Localization

CVPR 2017 · Architectures and efficiency › RNNs and normalization · approach: Differentiable optimization layers · 637 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train DSAC end-to-end rather than componentwise (ablation)DSAC camera localization pipeline · 7-Scenes Completeaccuracy: 60.3% -> 62.5%; +2.2%Helped
Train SoftAM end-to-end rather than componentwise (ablation)SoftAM camera localization pipeline · 7-Scenes Completeaccuracy: 61.6% -> 57.8%; -3.8%Hurt
Use end-to-end DSAC instead of Brachmann et al.'s pipeline7-Scenes camera localization · 7-Scenes Completeaccuracy: 55.2% -> 62.5%; 7.3%Helped
Use probabilistic hypothesis selection instead of soft argmax selection (ablation)End-to-end camera localization pipeline · 7-Scenes Completeaccuracy: 57.8% -> 62.5%Helped
Use probabilistic hypothesis selection instead of deterministic RANSAC selection (ablation)Componentwise-trained camera localization pipeline · 7-Scenes Completeaccuracy: 61.0% -> 60.3%; -0.7%Hurt
Train both the Coordinate CNN and Score CNN end-to-end (ablation)DSAC camera localization pipeline · 7-Scenes CompleteaccuracyHelped
Use soft argmax, which averages candidate hypotheses, instead of hard probabilistic selection (ablation)Differentiable RANSAC camera localization · 7-Scenesaccuracy: 62.5% -> 57.8% (Complete, end-to-end)Hurt
Replace soft argmax selection with probabilistic selection after end-to-end training (ablation)Differentiable RANSAC camera localization · 7-Scenes Completeaccuracy: 57.2% -> 62.4%Helped

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