Generalizable Novel View Synthesis

Generalizable novel view synthesis aims to create realistic images from unseen viewpoints, going beyond traditional methods limited to specific scenes or objects. Current research focuses on improving the efficiency and generalization capabilities of neural radiance fields (NeRFs) and related architectures like Gaussian splatting, often incorporating techniques like stereo matching, transformer networks, and contrastive learning to enhance accuracy and robustness. These advancements are driving progress towards real-time rendering and enabling applications in augmented and virtual reality, as well as bridging the gap between synthetic and real-world data for more practical deployment.