Results 11 to 20 of about 2,907,382 (374)
Masked Autoencoders for Point Cloud Self-supervised Learning [PDF]
European Conference on Computer Vision, 2022 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 point cloud self-supervised learning,
Yatian Pang +5 more
semanticscholar +1 more source
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding [PDF]
Computer Vision and Pattern Recognition, 2022 Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
Mohamed Afham +5 more
semanticscholar +1 more source
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [PDF]
Computer Vision and Pattern Recognition, 2021 We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT [8] to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a
Xumin Yu +5 more
semanticscholar +1 more source
PCT: Point cloud transformer [PDF]
Computational Visual Media, 2020 The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning.
Meng-Hao Guo +5 more
semanticscholar +1 more source
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Yin Zhou, Oncel Tuzel
semanticscholar +1 more source
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [PDF]
IEEE International Conference on Computer Vision, 2021 Point clouds captured in real-world applications are of-ten incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many ...
Xumin Yu +5 more
semanticscholar +1 more source
Diffusion Probabilistic Models for 3D Point Cloud Generation [PDF]
Computer Vision and Pattern Recognition, 2021 We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
Shitong Luo, Wei Hu
semanticscholar +1 more source
PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud [PDF]
Computer Vision and Pattern Recognition, 2018 In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain ...
Shaoshuai Shi +2 more
semanticscholar +1 more source
Cloud Computing and Grid Computing 360-Degree Compared [PDF]
Grid Computing Environments, 2008 Cloud computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for cloud computing and there seems to be no consensus on what a cloud is.
Ian T Foster +3 more
semanticscholar +1 more source
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data [PDF]
IEEE International Conference on Computer Vision, 2019 Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-
M. Uy +4 more
semanticscholar +1 more source