2 citations · 3 across the 5 of their papers we have counts for
8 papers
ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
Guangda Ji, Silvan Weder, Francis Engelmann +2
Neural network performance scales with both model size and data volume, as shown in both language and image processing. This requires scaling-friendly architectures and large datas…
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction
Silvan Weder, Francis Engelmann, Johannes L. Schönberger +3
We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly appli…
LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
Silvan Weder, Hermann Blum, Francis Engelmann +1
Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire. This work introduces a fully automated 2D/3D labeling framework that, wit…
Removing Objects From Neural Radiance Fields
Silvan Weder, Guillermo Garcia-Hernando, Aron Monszpart +4
Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Befo…
Learning Online Multi-Sensor Depth Fusion
Erik Sandström, Martin R. Oswald, Suryansh Kumar +4
Many hand-held or mixed reality devices are used with a single sensor for 3D reconstruction, although they often comprise multiple sensors. Multi-sensor depth fusion is able to sub…
DeepSurfels: Learning Online Appearance Fusion
Marko Mihajlovic, Silvan Weder, Marc Pollefeys +1
We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information. DeepSurfels combines explicit and neural building blocks to jointly encode geom…