Oct 31, 2022 · We propose a new self-supervised pretraining method that targets large-scale 3D scenes. We pretrain commonly used point-based and voxel-based model ...
In this paper, we propose a new self- supervised pretraining method that targets large-scale 3D scenes. We pretrain commonly used point-based and voxel-based ...
Jun 20, 2022 · A new self-supervised masked occupancy pre-training method called Occupancy-MAE, specifically designed for voxel-based large-scale outdoor LiDAR point clouds.
In this research, we propose a mask voxel autoencoder network for pre-training large-scale point clouds, dubbed Voxel-MAE.
Oct 9, 2023 · Occupancy-MAE takes advantage of the gradually sparse voxel occupancy structure of outdoor LiDAR point clouds and incorporates a range-aware ...
We propose DepthContrast- an easy to implement self- supervised method that works across model architectures, input data formats, indoor/outdoor, single/multi- ...
We propose Voxel-MAE, a method for deploying. MAE-style self-supervised pre-training on voxelized point clouds, and evaluate it on nuScenes, a large-scale.
Mar 1, 2024 · The goal of SSL is to pre-train an encoder on an unlabeled, large-scale point cloud dataset (source domain), and to transfer the well-trained ...
We formulate the point-cloud pre-training task as a semi-supervised problem, which leverages the few-shot labeled and massive unlabeled point-cloud data to ...
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This approach aims to learn generic and useful point cloud representations from unlabeled data, circumventing the need for extensive manual annotations. In this ...