Neural Radiance Field
Neural Radiance Fields (NeRFs) are a powerful technique for creating realistic 3D scene representations from 2D images, aiming to reconstruct both geometry and appearance. Current research focuses on improving efficiency and robustness, exploring variations like Gaussian splatting for faster rendering and adapting NeRFs for diverse data modalities (LiDAR, infrared, ultrasound) and challenging conditions (low light, sparse views). This technology has significant implications for various fields, including autonomous driving, robotics, medical imaging, and virtual/augmented reality, by enabling high-fidelity 3D scene modeling and novel view synthesis from limited input data.
Papers
ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization
Wenzhao Li, Tianhao Wu, Fangcheng Zhong, Cengiz Oztireli
Blending-NeRF: Text-Driven Localized Editing in Neural Radiance Fields
Hyeonseop Song, Seokhun Choi, Hoseok Do, Chul Lee, Taehyeong Kim
Pose Modulated Avatars from Video
Chunjin Song, Bastian Wandt, Helge Rhodin
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)
Ange Lou, Yamin Li, Xing Yao, Yike Zhang, Jack Noble
Efficient View Synthesis with Neural Radiance Distribution Field
Yushuang Wu, Xiao Li, Jinglu Wang, Xiaoguang Han, Shuguang Cui, Yan Lu
HollowNeRF: Pruning Hashgrid-Based NeRFs with Trainable Collision Mitigation
Xiufeng Xie, Riccardo Gherardi, Zhihong Pan, Stephen Huang
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Kun Wang, Zhiqiang Yan, Huang Tian, Zhenyu Zhang, Xiang Li, Jun Li, Jian Yang
Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing
Jie Zhang, Pengcheng Shi, Zaiwang Gu, Yiyang Zhou, Zhi Wang
Watch Your Steps: Local Image and Scene Editing by Text Instructions
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G. Derpanis, Igor Gilitschenski
Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language
Francesco Taioli, Federico Cunico, Federico Girella, Riccardo Bologna, Alessandro Farinelli, Marco Cristani
Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases
Eugen Šlapak, Enric Pardo, Matúš Dopiriak, Taras Maksymyuk, Juraj Gazda
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields
Zeke Xie, Xindi Yang, Yujie Yang, Qi Sun, Yixiang Jiang, Haoran Wang, Yunfeng Cai, Mingming Sun