167 citations · 935 across the 83 of their papers we have counts for
16 papers · 1 filter
Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve
Thomas Schöps, Viktor Larsson, Marc Pollefeys +1
Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus ofte…
DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing
Shaohui Liu, Yinda Zhang, Songyou Peng +3
We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance funct…
Slanted Stixels: A way to represent steep streets
Daniel Hernandez-Juarez, Lukas Schneider, Pau Cebrian +6
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather re…
Learned Semantic Multi-Sensor Depth Map Fusion
Denys Rozumnyi, Ian Cherabier, Marc Pollefeys +1
Volumetric depth map fusion based on truncated signed distance functions has become a standard method and is used in many 3D reconstruction pipelines. In this paper, we are general…
To Learn or Not to Learn: Visual Localization from Essential Matrices
Qunjie Zhou, Torsten Sattler, Marc Pollefeys +1
Visual localization is the problem of estimating a camera within a scene and a key component in computer vision applications such as self-driving cars and Mixed Reality. State-of-t…
Large-scale, real-time visual-inertial localization revisited
Simon Lynen, Bernhard Zeisl, Dror Aiger +5
The overarching goals in image-based localization are scale, robustness and speed. In recent years, approaches based on local features and sparse 3D point-cloud models have both do…