In cooperative, connected, and automated mobility (CCAM), the more automated vehicles
can perceive, model, and analyze the surrounding environment, the more they become …

Abstract We present Block-NeRF, a variant of Neural Radiance Fields that can represent
large-scale environments. Specifically, we demonstrate that when scaling NeRF to render …

Abstract Neural Radiance Field (NeRF) significantly degrades when only a limited number
of views are available. To complement the lack of 3D information, depth-based models, such …

We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale
visual captures spanning buildings or even multiple city blocks collected primarily from …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Very recently neural implicit rendering techniques have been rapidly evolved and shown
great advantages in novel view synthesis and 3D scene reconstruction. However, existing …

Modeling dynamic scenes is important for many applications such as virtual reality and
telepresence. Despite achieving unprecedented fidelity for novel view synthesis in dynamic …

This paper addresses the challenge of efficiently reconstructing volumetric videos of
dynamic humans from sparse multi-view videos. Some recent works represent a dynamic …

We are witnessing an explosion of neural implicit representations in computer vision and
graphics. Their applicability has recently expanded beyond tasks such as shape generation …

Reconstructing 3D indoor scenes from 2D images is an important task in many computer
vision and graphics applications. A main challenge in this task is that large texture-less …