Results 251 to 260 of about 8,359,557 (286)

Bridging Performance and Fate: A Framework for Sustainable, Composite‐Based Flexible Electronic Systems

Advanced Electronic Materials, EarlyView.
Composite‐based flexible electronics integrate biodegradable polymers, conductive networks, and multilayer device architectures to balance electrical performance, mechanical durability, and controlled degradation. Interfacial engineering and encapsulation regulate transport stability and operational lifetime, while programmed disassembly enables ...
Sayam, Sangho Cho
wiley   +1 more source

Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions. [PDF]

Sensors (Basel)
Ghose P, Bashir A, Zahid A.
europepmc   +1 more source

Controlling diverse robots by inferring Jacobian fields with deep networks. [PDF]

Nature
Li SL, Zhang A, Chen B, Matusik H, Liu C, Rus D, Sitzmann V.   +6 more
europepmc   +1 more source

PoseNeRF: In Situ 3D Reconstruction Method Based on Joint Optimization of Pose and Neural Radiation Field for Smooth and Weakly Textured Aeroengine Blade. [PDF]

Sensors (Basel)
Xiao Y, Wu X, Yin Y, Cai Y, Hou Y.
europepmc   +1 more source

Deep learning in plant phenotyping: the first ten years. [PDF]

Plant Phenomics
Ubbens J, Stavness I, Pound MP, Guo W.
europepmc   +1 more source

BirdNeRF: fast neural reconstruction of large-scale scenes from aerial imagery. [PDF]

Sci Rep
Zhang H, Xue Y, Liao M, Lao Y.
europepmc   +1 more source

TeaNeRF: an integrated 3D visual perception pipeline for tea bud harvesting. [PDF]

Front Plant Sci
Chen W, Li X, Rao L, Xia X.
europepmc   +1 more source

Dynamic Appearance Particle Neural Radiance Field [PDF]

IEEE Transactions on Circuits and Systems for Video Technology
Neural Radiance Fields (NeRFs) have shown great potential in modeling 3D scenes. Dynamic NeRFs extend this model by capturing time-varying elements, typically using deformation fields.
Yusheng Xiang, Ancheng Lin
exaly   +2 more sources

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A Survey on Neural Radiance Fields

ACM Computing Surveys
View synthesis is a fundamental task in computer vision, known for its significantly higher complexity compared to conventional vision problems. The introduction of Neural Radiance Fields (NeRF) marked a major breakthrough in this field, substantially improving previous methods and pushing view synthesis to unprecedented levels.
Yun Liao, Yide Di, Hao Zhou, Kaijun Zhu, Mingyu Lu, Qing Duan, Junhui Liu   +6 more
openaire   +2 more sources

Multimodal Neural Radiance Field

2023 IEEE International Conference on Robotics and Automation (ICRA), 2023
Haidong Zhu, Yuyin Sun, Chi Liu, Lu Xia, Jiajia Luo, Nan Qiao 0009, Ram Nevatia, Cheng-Hao Kuo   +7 more
openaire   +1 more source