atlas

Research map / Point clouds / Shape and surface completion

Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction From Raw Point Clouds

CVPR 2021 · Point clouds › Shape and surface completion · approach: Unsupervised shape correspondence · 32 citations (OpenAlex)

Check your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the proposed geometric initialization instead of random initialization (ablation)SAIL-S3 · Shape reconstructionsurface qualityHelped
Use local sign flipping rather than switching it off (ablation)SAIL-S3 · Shape reconstructionsurface qualityHelped
Use weighted interpolation of local fields instead of max pooling or average pooling (ablation)SAIL-S3 · Shape reconstructionsurface qualityMixed
Use SAIL-S3 rather than P2SThreeDScans noisy point clouds · ThreeDScans CD@0.01CD: 0.008 -> 0.004Helped
Use SAIL-S3 rather than P2SThreeDScans noisy point clouds · ThreeDScans CD@0.005CD: 0.008 -> 0.003Helped
Use SAIL-S3 rather than SPSRThreeDScans noisy point clouds · ThreeDScans CD@0.001CD: 0.003 -> 0.003No clear effect

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy