Radiance Field
Radiance fields are neural representations of 3D scenes that enable novel view synthesis and other advanced capabilities. Current research focuses on improving efficiency and realism, particularly through the use of Gaussian splatting, which offers faster rendering and better handling of view-dependent effects, as well as addressing challenges like handling dynamic scenes, inconsistent lighting conditions, and limited data. These advancements are significant for applications in robotics, virtual and augmented reality, and computer graphics, offering more realistic and efficient 3D scene modeling and manipulation.
Papers
Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping
Adam Rashid, Satvik Sharma, Chung Min Kim, Justin Kerr, Lawrence Chen, Angjoo Kanazawa, Ken Goldberg
ChromaDistill: Colorizing Monochrome Radiance Fields with Knowledge Distillation
Ankit Dhiman, R Srinath, Srinjay Sarkar, Lokesh R Boregowda, R Venkatesh Babu
Scene-Generalizable Interactive Segmentation of Radiance Fields
Songlin Tang, Wenjie Pei, Xin Tao, Tanghui Jia, Guangming Lu, Yu-Wing Tai
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Muyu Xu, Fangneng Zhan, Jiahui Zhang, Yingchen Yu, Xiaoqin Zhang, Christian Theobalt, Ling Shao, Shijian Lu
3D Motion Magnification: Visualizing Subtle Motions with Time Varying Radiance Fields
Brandon Y. Feng, Hadi Alzayer, Michael Rubinstein, William T. Freeman, Jia-Bin Huang
Mirror-NeRF: Learning Neural Radiance Fields for Mirrors with Whitted-Style Ray Tracing
Junyi Zeng, Chong Bao, Rui Chen, Zilong Dong, Guofeng Zhang, Hujun Bao, Zhaopeng Cui