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Found 4 papers, 2 papers with code
PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling
In this work, the flexible upsampling rates are achieved via edge vector based affine combinations, and a novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed.
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
Ranked #8 on
3D Point Cloud Classification
on ScanObjectNN
PU-EVA: An Edge-Vector Based Approximation Solution for Flexible-Scale Point Cloud Upsampling
In this work, the arbitrary point clouds upsampling rates are achieved via edge-vector based affine combinations, and a novel design of Edge-Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed.
Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors
Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner.
