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
PH-Dropout: Prctical Epistemic Uncertainty Quantification for View Synthesis
Chuanhao Sun, Thanos Triantafyllou, Anthos Makris, Maja Drmač, Kai Xu, Luo Mai, Mahesh K. Marina
LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting
Qifeng Chen, Sheng Yang, Sicong Du, Tao Tang, Peng Chen, Yuchi Huo
TeX-NeRF: Neural Radiance Fields from Pseudo-TeX Vision
Chonghao Zhong, Chao Xu
3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object Detection
Yang Cao, Yuanliang Jv, Dan Xu
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians
Shuyi Jiang, Qihao Zhao, Hossein Rahmani, De Wen Soh, Jun Liu, Na Zhao
AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction
Jingnan Gao, Zhuo Chen, Yichao Yan, Xiaokang Yang
GMT: Enhancing Generalizable Neural Rendering via Geometry-Driven Multi-Reference Texture Transfer
Youngho Yoon, Hyun-Kurl Jang, Kuk-Jin Yoon
Cafca: High-quality Novel View Synthesis of Expressive Faces from Casual Few-shot Captures
Marcel C. Bühler, Gengyan Li, Erroll Wood, Leonhard Helminger, Xu Chen, Tanmay Shah, Daoye Wang, Stephan Garbin, Sergio Orts-Escolano, Otmar Hilliges, Dmitry Lagun, Jérémy Riviere, Paulo Gotardo, Thabo Beeler, Abhimitra Meka, Kripasindhu Sarkar