As a bridge between physical objects and as-built models, image-based 3D reconstruction
performs a vital role by generating point cloud models, mesh models, textured models, and …

Neural implicit representations have recently demonstrated compelling results on dense
Simultaneous Localization And Map** (SLAM) but suffer from the accumulation of errors …

Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

Three-dimensional reconstruction is a key technology employed to represent virtual reality in
the real world, which is valuable in computer vision. Large-scale 3D models have broad …

Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple
2D input views. However, a well-known drawback of NeRFs is the less-than-ideal …

Public engagement in protecting architectural heritage is a critical component of sustainable
development. This study has developed an innovative single drone-based 3D reconstruction …

Generalizable NeRF aims to synthesize novel views for unseen scenes. Common practices
involve constructing variance-based cost volumes for geometry reconstruction and encoding …

An important objective in computer vision is to analyze multiple images and subsequently
reconstruct the shape and structure in 3D. Traditional multi-view 3D reconstruction …

Abstract Unsupervised Multi-View Stereo (MVS) methods have achieved promising progress
recently. However, previous methods primarily depend on the photometric consistency …

Utilizing multi-view inputs to synthesize novel-view images Neural Radiance Fields (NeRF)
have emerged as a popular research topic in 3D vision. In this work we introduce a …