Jul 4, 2022 · In this paper, we propose masked Autoencoders in 3D point cloud representation learning (abbreviated as MAE3D), a novel autoencoding paradigm for self-supervised learning. …

In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal …

Sep 28, 2022 · This paper proposes Point Cloud Masked Autoencoder (PCMAE), which can provide pre-training for most voxel-based point cloud object detection algorithms. PCMAE …

Jan 16, 2024 · Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike …

To address these issues, we propose LSV-MAE, a masked autoencoding pre-training scheme designed for voxel representations. We pre-train the backbone to reconstruct the masked …

In this paper, we propose a novel scheme of masked autoencoders for 3D point cloud self-supervised learning, addressing the special challenges posed by point cloud, including …

Mar 13, 2022 · Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning, addressing the challenges posed by point cloud's properties, …

To address the fusion of multi-modal point cloud and im-age data, we propose PiMAE, a simple yet effective pipeline that learns strong 3D and 2D features by increasing their interaction.

Motivated by the success of a masked autoencoder in 3-D point-cloud-based learning, this study proposes an innovative framework for self-supervised learning (SSL) on 3-D point clouds with …

4 days ago · Yu, X. et al. Point-Bert: Pre-training 3d point cloud transformers with masked point modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …