A5039280701- 3D Shape Modeling and Analysis
- Face recognition and analysis
- Genetic Mapping and Diversity in Plants and Animals
- Robotics and Sensor-Based Localization
- Advanced Vision and Imaging
- Remote Sensing in Agriculture
- Genomics and Phylogenetic Studies
- Facial Rejuvenation and Surgery Techniques
- Generative Adversarial Networks and Image Synthesis
- Smart Agriculture and AI
- Microbial Community Ecology and Physiology
Ningxia University
2022-2023
Beijing Institute of Genomics
2023
Chinese Academy of Sciences
2023
University of Chinese Academy of Sciences
2023
Abstract The National Genomics Data Center (NGDC), which is a part of the China for Bioinformation (CNCB), provides family database resources to support global academic and industrial communities. With rapid accumulation multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands updates core through big archiving, integrative analysis value-added curation. Importantly, NGDC collaborates closely with major international databases initiatives ensure seamless exchange...
Abstract High-throughput plant phenotype acquisition technologies have been extensively utilized in phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, diseases identification biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive i-traits derived from high-throughput phenotyping...
Single-view three-dimensional (3D) object reconstruction has always been a long-term challenging task. Objects with complex topologies are hard to accurately reconstruct, which makes existing methods suffer from blurring of shape boundaries between multiple components in the object. Moreover, most them cannot balance learning global geometric structure information and local detail information. In this article, we propose multi-scale edge-guided network (MEGLN) utilize edge guiding better...
In this work, we propose Geometry Normal Consistency Loss (GNC) for 3D face reconstruction and dense alignment. The existing methods based on the strong constraints of 3DMM parameter regression only consider reducing error between 68 landmarks, while they rarely geometric contour structure relation face. Instead, take into account discrete landmarks as loss constrain by introducing geometry area normal consistency loss, which naturally defines holistic local detail, select inverted triangle...