Results 101 to 110 of about 7,018 (281)

SemAlign3D: Multi-Dataset Point Cloud Segmentation with Learnable Class Prompts and KNN Multi-Scale Attention

Remote Sensing
Point cloud segmentation is a core technology in remote sensing, enabling the extraction of rich semantic information from complex scenes. Existing methods struggle with semantic inconsistency across multiple heterogeneous datasets in complex urban ...
Xuanhong Bao, Hao Zhang
doaj   +1 more source

Visualizing ESG Signaling Through User‐Generated Content: A Strategic Foresight Framework for Symbolic Legitimacy in Hospitality Branding

Business Strategy and the Environment, EarlyView.
ABSTRACT Social media platforms today have become essential for consumer‐brand interactions, with visual content playing a pivotal role in shaping engagement and brand perception. Although text‐based user‐generated content (UGC) has been widely studied, the potential of visual UGC, particularly in the travel, tourism and hospitality (TTH) sector ...
Chinchu Abraham, Pabitra Mitra, Sujoy Bhattacharya   +2 more
wiley   +1 more source

Traffic Scene Semantic Segmentation Enhancement Based on Cylinder3D with Multi-Scale 3D Attention

Sensors
With the rapid development of 3D sensor technology, point cloud semantic segmentation has found widespread applications in autonomous driving, remote sensing, mapping, and industrial manufacturing.
Yun Bai, Xu Zhou, Yuxuan Gong, Yuanhao Huang   +3 more
doaj   +1 more source

ESG Ratings and Firms' Engagement in Global Innovation Ecosystems: Implications for Green Innovation Capacity

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT The increasing salience of climate change has intensified attention to the roe of ESG ratings in shaping firms' green innovation. We examine the link between ESG performance and green innovation, highlighting the role of participation in global innovation networks.
Miaomiao Tao, David Roubaud, Mingzhi Zhang, Qianli Ma   +3 more
wiley   +1 more source

Multimodal Point Cloud Semantic Segmentation With Virtual Point Enhancement

CoRR
LiDAR-based 3D point cloud recognition has been proven beneficial in various applications. However, the sparsity and varying density pose a significant challenge in capturing intricate details of objects, particularly for medium-range and small targets.
Zaipeng Duan, Xuzhong Hu, Pei An, Jie Ma 0003   +3 more
openaire   +2 more sources

Orchestrating Green Transformation: How AI Adoption Enables Corporate Carbon Neutrality

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT As carbon neutrality has become a central goal of global climate governance, how firms achieve low‐carbon transformation has emerged as a critical research issue. However, prior studies have primarily focused on macro‐ or industry‐level analyses, offering limited and fragmented insights into how digital technologies—particularly AI—affect firm‐
Xiaonan Dong, Sungjin Son
wiley   +1 more source

Semantic segmentation of point clouds of ancient buildings based on weak supervision

Heritage Science
Semantic segmentation of point clouds of ancient buildings plays an important role in Historical Building Information Modelling (HBIM). As the annotation task of point cloud of ancient architecture is characterised by strong professionalism and large ...
Jianghong Zhao, Haiquan Yu, Xinnan Hua, Xin Wang, Jia Yang, Jifu Zhao, Ailin Xu   +6 more
doaj   +1 more source

LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds

, 2022
Semantic segmentation of LiDAR point clouds is an important task in autonomous driving. However, training deep models via conventional supervised methods requires large datasets which are costly to label. It is critical to have label-efficient segmentation approaches to scale up the model to new operational domains or to improve performance on rare ...
Minghua Liu, Yin Zhou, Charles R. Qi, Boqing Gong, Hao Su 0001, Dragomir Anguelov   +5 more
openaire   +2 more sources

PCL: Point Contrast and Labeling for Weakly Supervised Point Cloud Semantic Segmentation [PDF]

Point cloud semantic segmentation is a fundamental task in 3D scene understanding and has recently achieved remarkable progress. The success of existing approaches is attributed to recent advanced deep networks for point clouds and the availability of a ...
Wu, Q, Pang, S, Zhou, T, Du, A, Zhang, J
core   +1 more source

Efficient training of semantic point cloud segmentation via active learning [PDF]

, 2020
With the development of LiDAR and photogrammetric techniques, more and more point clouds are available with high density and in large areas. Point cloud interpretation is an important step before many real applications like 3D city modelling.
Lafarge, F., Cao, Y., Paparoditis, N., Lin, Y., Fuse, T., Vosselman, G., Mallet, C., Yang, M. Y., Remondino, F., Toschi, I.   +9 more
core   +1 more source