Novel View Synthesis
Novel view synthesis (NVS) aims to generate realistic images from viewpoints not directly captured, reconstructing 3D scenes from 2D data. Current research heavily utilizes implicit neural representations, such as neural radiance fields (NeRFs) and 3D Gaussian splatting, focusing on improving efficiency, handling sparse or noisy input data (including single-view scenarios), and enhancing the realism of synthesized views, particularly for complex scenes with dynamic elements or challenging lighting conditions. These advancements have significant implications for various fields, including robotics, cultural heritage preservation, and virtual/augmented reality applications, by enabling more accurate 3D modeling and more immersive experiences.
Papers
Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings
Baixin Xu, Jiarui Zhang, Kwan-Yee Lin, Chen Qian, Ying He
GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images
Jianchuan Chen, Wentao Yi, Liqian Ma, Xu Jia, Huchuan Lu
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Marie-Julie Rakotosaona, Fabian Manhardt, Diego Martin Arroyo, Michael Niemeyer, Abhijit Kundu, Federico Tombari
NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameters
Hannah Schieber, Fabian Deuser, Bernhard Egger, Norbert Oswald, Daniel Roth