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Research map / Point clouds / Registration and matching

Deterministic Point Cloud Registration via Novel Transformation Decomposition

CVPR 2022 · Point clouds › Registration and matching · approach: Non-rigid point set registration · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Decompose the 6-DOF transform into sequential (2+1)-DOF and (1+2)-DOF searches using branch and boundPoint cloud registration · 3DMatchSR (%): 91.68 -> 92.98Helped
Use TR-DE rather than PointDSCPoint cloud registration · 3DMatchTime (s): 0.02 -> 0.16Hurt
Use TR-DE rather than PointDSCPoint cloud registration · 3DLoMatch with FCGF descriptorsSR (%)Mixed
Use TR-DE rather than PointDSCPoint cloud registration · 3DLoMatch with Predator descriptorsSR (%)Helped
Use TR-DE rather than RANSAC-10kPoint cloud registration · KITTI with FCGF descriptorsSR (%): 98.02 -> 98.2Helped
Use TR-DE rather than PointDSCPoint cloud registration · KITTI with FCGF descriptorsRE (°): 0.33 -> 0.38Hurt
Use TR-DE rather than PointDSCPoint cloud registration · KITTI with FCGF descriptorsTE (cm): 20.99 -> 18Helped
Replace pairwise registration with TR-DEMulti-way registration pipeline · Augmented ICL-NUIM, AVGATE (cm): 17.61 -> 14.2Helped
Use the proposed (2+1)/(1+2)-DOF decomposition and nested branch-and-bound searchDeterministic point cloud registration · Synthetic dataset, correspondence count increased from 2000 to 4000Time (s)Helped

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