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 …

We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our
model uses d-choose-2 planes to represent a d-dimensional scene, providing a seamless …

We introduce LightGlue, a deep neural network that learns to match local features across
images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse …

Three-dimensional mapping is an increasingly important feature for recent photogrammetry
and remote sensing (RS) systems. Currently, unmanned aerial vehicles (UAVs) have …

We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale
visual captures spanning buildings or even multiple city blocks collected primarily from …

Camera pose estimation is a long-standing computer vision problem that to date often relies
on classical methods, such as handcrafted keypoint matching, RANSAC and bundle …

We present a learning-based method for synthesizingnovel views of complex scenes using
only unstructured collections of in-the-wild photographs. We build on Neural Radiance …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

We propose a novel framework for finding correspondences in images based on a deep
neural network that, given two images and a query point in one of them, finds its …

Image Matching is a core component of all best-performing algorithms and pipelines in 3D
vision. Yet despite matching being fundamentally a 3D problem, intrinsically linked to …