Search Results for author:
Found 7 papers, 4 papers with code
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction
Using these main contributions, our method can enable scenarios with real-time constraints and can scale to arbitrary scene sizes by processing and updating the scene only in a local region defined by the new measurement.
LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire.
Removing Objects From Neural Radiance Fields
We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.
Learning Online Multi-Sensor Depth Fusion
Multi-sensor depth fusion is able to substantially improve the robustness and accuracy of 3D reconstruction methods, but existing techniques are not robust enough to handle sensors which operate with diverse value ranges as well as noise and outlier statistics.
DeepSurfels: Learning Online Appearance Fusion
We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information.
NeuralFusion: Online Depth Fusion in Latent Space
We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space.
RoutedFusion: Learning Real-time Depth Map Fusion
To this end, we present a novel real-time capable machine learning-based method for depth map fusion.
