8 citations · 12 across the 3 of their papers we have counts for
6 papers
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction
Evin Pınar Örnek, Shristi Mudgal, Johanna Wald +3
There have been numerous recently proposed methods for monocular depth prediction (MDP) coupled with the equally rapid evolution of benchmarking tools. However, we argue that MDP i…
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Shun-Cheng Wu, Johanna Wald, Keisuke Tateno +2
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semanti…
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
Johanna Wald, Torsten Sattler, Stuart Golodetz +2
Long-term camera re-localization is an important task with numerous computer vision and robotics applications. Whilst various outdoor benchmarks exist that target lighting, weather…
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions
Johanna Wald, Helisa Dhamo, Nassir Navab +1
Scene understanding has been of high interest in computer vision. It encompasses not only identifying objects in a scene, but also their relationships within the given context. Wit…
RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
Johanna Wald, Armen Avetisyan, Nassir Navab +2
In this work, we introduce the task of 3D object instance re-localization (RIO): given one or multiple objects in an RGB-D scan, we want to estimate their corresponding 6DoF poses…
Fully-Convolutional Point Networks for Large-Scale Point Clouds
Dario Rethage, Johanna Wald, Jürgen Sturm +2
This work proposes a general-purpose, fully-convolutional network architecture for efficiently processing large-scale 3D data. One striking characteristic of our approach is its ab…