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Research map / Point clouds / Shape and surface completion

Learning Deformable Tetrahedral Meshes for 3D Reconstruction

NeurIPS 2020 · Point clouds › Shape and surface completion · approach: Unsupervised shape correspondence · 58 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add vertex deformation to fixed tetrahedral occupancy (ablation)DefTet · ShapeNet point-cloud reconstructionMean-IoU: 68.98 -> 76.35Helped
Use the DefTet encoder architecture with OccNetOccNet · ShapeNet point-cloud reconstructionMean-IoU: 62.84 -> 76.93Helped
Use DefTet instead of OccNetPoint-cloud reconstruction · ShapeNet point-cloud reconstructionTime (ms): 728.36 -> 61.39Helped
Apply Marching Tet to the DefTet prediction (ablation)DefTet · ShapeNet single-image reconstruction with 2D supervisionMean Chamfer distance: 3.6 -> 3.1Helped
Add vertex deformation rather than keep a fixed tetrahedral grid (ablation)DefTet · ShapeNet single-image reconstruction with 2D supervisionMean Chamfer distance: 4.5 -> 3.6Helped
Add vertex deformation while using Marching Tet (ablation)FixedTet + MTet · ShapeNet single-image reconstruction with 2D supervisionMean Chamfer distance: 3.5 -> 3.1Helped
Use amortized learning for meshing instead of optimization alone (ablation)DefTet tetrahedral meshing · Chair tetrahedral meshingRun Time (sec.): 1956.80 -> 49.13Helped
Use amortized learning instead of optimization alone (ablation)DefTet tetrahedral meshing · Chair tetrahedral meshingAMIPS: 3.37 -> 3.61Hurt
Use amortized learning instead of optimization alone (ablation)DefTet tetrahedral meshing · Chair tetrahedral meshingChamfer (e-3): 4.21 -> 4.27Hurt

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