Unmanned aerial vehicle (UAV) images have gained extensive attention in varying fields,
and the Structure from Motion (SfM) technique has become the gold standard for aerial …

In the last few decades, Structure from Motion (SfM) and visual Simultaneous Localization
and Map** (visual SLAM) techniques have gained significant interest from both the …

This paper introduces the Meshroom software and its underlying 3D computer vision
framework AliceVision. This solution provides a photogrammetry pipeline to reconstruct 3D …

We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with
Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and …

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical
flow and ego-motion estimation from videos. The three components are coupled by the …

Single-view depth prediction is a fundamental problem in computer vision. Recently, deep
learning methods have led to significant progress, but such methods are limited by the …

We introduce a comprehensive benchmark for local features and robust estimation
algorithms, focusing on the downstream task—the accuracy of the reconstructed camera …

Abstract Graph Neural Networks (GNNs) with attention have been successfully applied for
learning visual feature matching. However, current methods learn with complete graphs …

We present a benchmark for image-based 3D reconstruction. The benchmark sequences
were acquired outside the lab, in realistic conditions. Ground-truth data was captured using …

The classical matching pipeline used for visual localization typically involves three steps:(i)
local feature detection and description,(ii) feature matching, and (iii) outlier rejection …