20 citations · 33 across the 10 of their papers we have counts for
12 papers
3D Scene Graphs: Open Challenges and Future Directions
Dennis Rotondi, Francesco Argenziano, Sebastian Koch +10
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Thei…
OVI-MAP:Open-Vocabulary Instance-Semantic Mapping
Zilong Deng, Federico Tombari, Marc Pollefeys +2
Incremental open-vocabulary 3D instance-semantic mapping is essential for autonomous agents operating in complex everyday environments. However, it remains challenging due to the n…
Unified Semantic Transformer for 3D Scene Understanding
Sebastian Koch, Johanna Wald, Hidenobu Matsuki +3
Holistic 3D scene understanding involves capturing and parsing unstructured 3D environments. Due to the inherent complexity of the real world, existing models have predominantly be…
RelationField: Relate Anything in Radiance Fields
Sebastian Koch, Johanna Wald, Mirco Colosi +4
Neural radiance fields are an emerging 3D scene representation and recently even been extended to learn features for scene understanding by distilling open-vocabulary features from…
Search3D: Hierarchical Open-Vocabulary 3D Segmentation
Ayca Takmaz, Alexandros Delitzas, Robert W. Sumner +3
Open-vocabulary 3D segmentation enables exploration of 3D spaces using free-form text descriptions. Existing methods for open-vocabulary 3D instance segmentation primarily focus on…
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
Francis Engelmann, Ayca Takmaz, Jonas Schult +25
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023. The goal of this wo…
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…