Neural Radiance Field (NeRF) has recently become a significant development in the field of
Computer Vision, allowing for implicit, neural network-based scene representation and …

In the context of automated driving, navigating through challenging urban environments with
dynamic objects, large-scale scenes, and varying lighting/weather conditions, achieving …

Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields
(NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as …

Abstract Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in
capturing complex 3D scenes with high fidelity. However one persistent challenge that …

Abstract 3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at
rendering photorealistic novel views of complex scenes. However recovering a high-quality …

Abstract Training a Neural Radiance Field (NeRF) without pre-computed camera poses is
challenging. Recent advances in this direction demonstrate the possibility of jointly …

Radiance fields have demonstrated impressive performance in synthesizing novel views
from sparse input views yet prevailing methods suffer from high training costs and slow …

In recent years, neural implicit surface reconstruction methods have become popular for
multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these …

This paper presents a method to optimize Gaussian splatting with a limited number of
images while avoiding overfitting. Representing a 3D scene by combining numerous …

Emerging neural radiance fields (NeRF) are a promising scene representation for computer
graphics, enabling high-quality 3D reconstruction and novel view synthesis from image …