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magazén, 2022 The Space, Place, Sound, and Memory: Immersive Experiences of the Past project was led by dr James Cook, in collaboration with the Digital Documentation and Innovation team at Historic Environment Scotland, Soluis Heritage, the Binchois Consort, and ...
Cook, James, Mirashrafi, Sophia
doaj +2 more sources
Point Cloud Mamba: Point Cloud Learning via State Space Model
Proceedings of the AAAI Conference on Artificial IntelligenceRecently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying such architecture to more efficiently and effectively model point cloud data globally with linear computational complexity.
Zhang, Tao +7 more
openaire +3 more sources
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
Geometric Transformer for Fast and Robust Point Cloud Registration [PDF]
Computer Vision and Pattern Recognition, 2022 We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios, showing great potential in registration. They
Zheng Qin +5 more
semanticscholar +1 more source
GeoTransformer: Fast and Robust Point Cloud Registration With Geometric Transformer [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap ...
Zheng Qin +7 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
Stratified Transformer for 3D Point Cloud Segmentation [PDF]
Computer Vision and Pattern Recognition, 2022 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies.
Xin Lai +7 more
semanticscholar +1 more source
REGTR: End-to-end Point Cloud Correspondences with Transformers [PDF]
Computer Vision and Pattern Recognition, 2022 Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of ...
Zi Jian Yew, Gim Hee Lee
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