16 citations · 98 across the 47 of their papers we have counts for
57 papers · 1 filter
Unsupervised Anomaly Detection from Time-of-Flight Depth Images
Pascal Schneider, Jason Rambach, Bruno Mirbach +1
Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are mo…
Scale Invariant Semantic Segmentation with RGB-D Fusion
Mohammad Dawud Ansari, Alwi Husada, Didier Stricker
In this paper, we propose a neural network architecture for scale-invariant semantic segmentation using RGB-D images. We utilize depth information as an additional modality apart f…
Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes
Steve Dias Da Cruz, Bertram Taetz, Thomas Stifter +1
Learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learni…
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds
Ramy Battrawy, René Schuster, Mohammad-Ali Nikouei Mahani +1
The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density. For h…
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer +5
Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replace…
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching
Kumail Raza, René Schuster, Didier Stricker
Despite the remarkable progress of deep learning in stereo matching, there exists a gap in accuracy between real-time models and slower state-of-the-art models which are suitable f…