DiffPMAE: Diffusion Masked Autoencoders for Point Cloud Reconstruction
Point cloud streaming is increasingly getting popular, evolving into the norm for interactive service delivery and the future Metaverse. However, the substantial volume of data associated with point clouds presents numerous challenges, particularly in terms of high bandwidth consumption and large storage capacity. Despite various solutions proposed thus far, with a focus on point cloud compression, upsampling, and completion, these reconstruction-related methods continue to fall short in delivering high fidelity point cloud output. As a solution, in DiffPMAE, we propose an effective point cloud reconstruction architecture. Inspired by self-supervised learning concepts, we combine Masked Auto-Encoding and Diffusion Model mechanism to remotely reconstruct point cloud data. By the nature of this reconstruction process, DiffPMAE can be extended to many related downstream tasks including point cloud compression, upsampling and completion. Leveraging ShapeNet-55 and ModelNet datasets with over 60000 objects, we validate the performance of DiffPMAE exceeding many state-of-the-art methods in-terms of auto-encoding and downstream tasks considered.
Code (1)
Tasks
Point cloud reconstructionSelf-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation
Diffusion Transformers have recently shown remarkable effectiveness in generating high-quality 3D point clouds. However, training voxel-based diffusion models for high-resolution 3D voxels remains prohibitively expensive…
3D GenerationDenoisingPoint Cloud GenerationMasked Autoencoders in 3D Point Cloud Representation Learning
Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, self-supervised lear…
Point Cloud CompletionPoint cloud reconstructionRepresentation LearningSelf-Supervised LearningMasked Autoencoders for Point Cloud Self-supervised Learning
As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for poi…
3D Part Segmentation3D Point Cloud ClassificationFew-Shot 3D Point Cloud ClassificationFew-Shot Learning+1Block-to-Scene Pre-training for Point Cloud Hybrid-Domain Masked Autoencoders
Point clouds, as a primary representation of 3D data, can be categorized into scene domain point clouds and object domain point clouds based on the modeled content. Masked autoencoders (MAE) have become the mainstream pa…
ObjectPosition regressionSelf-Supervised LearningPAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment
No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference, which has achieved remarkable performance due to the utilization of deep learn…
Point Cloud Quality Assessment