The field of 3D scene representation and reconstruction is rapidly advancing, with a focus on improving the efficiency, accuracy, and scalability of existing methods. Recent research has explored the use of Gaussian splatting, neural radiance fields, and other techniques to enable fast and high-quality rendering of 3D scenes from sparse input views. Notable developments include the use of feedforward models, self-supervised learning, and optimal transport perspectives to improve the compactness and fidelity of 3D scene representations. These advancements have significant implications for applications such as augmented reality, robotic interaction, and computer vision.

Noteworthy papers in this area include the work on Complex-Valued Holographic Radiance Fields, which proposes a novel representation that optimizes 3D scenes without relying on intensity-based intermediaries, achieving 30x-10,000x speed improvements while maintaining on-par image quality. Another notable paper is UniForward, which presents a feed-forward Gaussian Splatting model that unifies 3D scene and semantic field reconstruction, enabling real-time reconstruction of 3D scenes and semantic fields from sparse-view images. TinySplat is also worth mentioning, as it proposes a complete feedforward approach for generating compact 3D scene representations, achieving over 100x compression for 3D Gaussian data generated by feedforward methods.