Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view synthesis …

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 …

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 …

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work
tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons …

Abstract 2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving
this problem due to their prior knowledge of the 3D world developed over years. Driven by …

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

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

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 …

Abstract Neural Radiance Field (NeRF) has recently emerged as a powerful representation
to synthesize photorealistic novel views. While showing impressive performance, it relies on …

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