凸最適化による大規模3次元復元
Building Rome with Convex Optimization
学習した深度を用いて2D特徴点を3Dに持ち上げるバンドル調整を凸緩和で大域最適に解き、CUDAベースの高速ソルバで大規模SfMを初期値不要で実現した。
分類: cs.RO, cs.CV, math.OC
原文アブストラクト
Global bundle adjustment is made easy by depth prediction and convex optimization. We (i) propose a scaled bundle adjustment (SBA) formulation that lifts 2D keypoint measurements to 3D with learned depth, (ii) design an empirically tight convex semidfinite program (SDP) relaxation that solves SBA to certfiable global optimality, (iii) solve the SDP relaxations at extreme scale with Burer-Monteiro factorization and a CUDA-based trust-region Riemannian optimizer (dubbed XM), (iv) build a structure from motion (SfM) pipeline with XM as the optimization engine and show that XM-SfM compares favorably with existing pipelines in terms of reconstruction quality while being significantly faster, more scalable, and initialization-free.
