Cloud Point Behavior of Poly(trifluoroethyl methacrylate) in Supercritical CO2–Toluene Mixtures [PDF]
MoleculesSupercritical CO2 (scCO2) is a versatile solvent for polymer processing; however, many partially fluorinated polymers exhibit limited solubility in neat scCO2. Organic cosolvents such as toluene can enhance polymer–solvent interactions, thereby improving
James R. Zelaya, Gary C. Tepper
doaj +2 more sources
Cloud point extraction coupled with back extraction: a green methodology in analytical chemistry. [PDF]
Forensic Sci Res, 2019 Recently, cloud point extraction (CPE) coupled with back extraction (BE) has been suggested as a promising alternative to liquid-liquid extraction. In CPE, non-ionic surfactants in aqueous solutions form micelles and the solution becomes turbid when ...
Kori S.
europepmc +2 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
Novel, energy efficient and green cloud point extraction: technology and applications in food processing. [PDF]
J Food Sci Technol, 2019 Arya SS +4 more
europepmc +2 more sources
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
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-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training [PDF]
Neural Information Processing Systems, 2022 Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to exploit masked autoencoding for learning 3D representations of irregular ...
Renrui Zhang +7 more
semanticscholar +1 more source
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following [PDF]
arXiv.org, 2023 We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space between 3D and multi-modalities, enabling many promising applications, e.g., any-
Ziyu Guo +10 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