Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

Abstract Optimizing and rendering Neural Radiance Fields is computationally expensive
due to the vast number of samples required by volume rendering. Recent works have …

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction,
achieving high quality novel view synthesis and fast rendering speed. However, 3DGS fails …

Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF)
volumes, such that they can be rendered and their dynamics simulated in a physically …

This paper presents a critical analysis of image-based 3D reconstruction using neural
radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional …

While surface-based view synthesis algorithms are appealing due to their low computational
requirements, they often struggle to reproduce thin structures. In contrast, more expensive …

In the last decade, deep learning (DL) has significantly impacted industry and science.
Initially largely motivated by computer vision tasks in 2-D imagery, the focus has shifted …

We introduce Deceptive-NeRF, a novel methodology for few-shot NeRF reconstruction,
which leverages diffusion models to synthesize plausible pseudo-observations to improve …

Numerous digital media repositories have been set up during recent decades, each
containing plenty of data about historic cityscapes. In contrast, digital 3D reconstructions of …

Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis
results, while allowing the rendering of high-resolution images in real-time. However …