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Research map / 3D vision / Depth and stereo

Power Bundle Adjustment for Large-Scale 3D Reconstruction

CVPR 2023 · 3D vision › Depth and stereo · approach: Feed-forward 3D reconstruction · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a power-series expansion of the inverse Schur complement instead of competing solversBundle adjustment on BAL · BAL, τ=0.1 and τ=0.01runtime and accuracy performance profileHelped
Use a power-series expansion of the inverse Schur complement instead of competing solversBundle adjustment on BAL · BAL, τ=0.003runtime and accuracy performance profileHelped
Use a power-series expansion of the inverse Schur complement instead of competing solversBundle adjustment on BAL · BAL, τ=0.001problems solved (%): 13% vs 54% at α = 1; 52% vs 88% at α = 3; 58% vs 98% at α = ∞Hurt
Replace dense LLᵀ factorization with PoBA for stochastic subproblemsSTBA, forming PoST · BAL, τ=0.1, 0.01, and 0.003runtime and accuracy performance profileHelped
Replace dense LLᵀ factorization with PoBA for stochastic subproblemsSTBA, forming PoST · BAL, τ=0.001accuracyNo clear effect
Increase the power-series order mPower-series preconditioner for CG · Ladybug-49total runtimeHurt
Increase the power-series order mPower-series preconditioner for CG · Ladybug-49CG iterationsHelped
Use PoBA-32 instead of PoBA-64PoBA · BAL, τ=0.001problems solved (%) at α=1: 13% -> 8%Hurt

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