Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

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 …

Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing
surfaces from multi-view images and synthesizing novel views. Unfortunately, existing …

Due to the limitations of LiDAR, such as its high cost, short service life and massive volume,
visual sensors with their lightweight and low cost are attracting more and more attention and …

Learning-based 3D reconstruction methods have shown impressive results. However, most
methods require 3D supervision which is often hard to obtain for real-world datasets …

Abstract Model-based human pose estimation is currently approached through two different
paradigms. Optimization-based methods fit a parametric body model to 2D observations in …

Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain
complex phenomena such as thin structures, evolving topology, translucency, scattering …

While deep learning has recently achieved great success on multi-view stereo (MVS),
limited training data makes the trained model hard to be generalized to unseen scenarios …

We propose a cost volume-based neural network for depth inference from multi-view
images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner …

Abstract We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D
reconstruction from multiple RGB images. Multi-view stereo (MVS) aims to reconstruct fine …