26 citations · 75 across the 21 of their papers we have counts for
6 papers · 1 filter
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images
Ramy Battrawy, René Schuster, Oliver Wasenmüller +2
We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images. We take the advantage of the high accuracy of LiDAR…
A Compact Light Field Camera for Real-Time Depth Estimation
Yuriy Anisimov, Oliver Wasenmüller, Didier Stricker
Depth cameras are utilized in many applications. Recently light field approaches are increasingly being used for depth computation. While these approaches demonstrate the technical…
DeLiO: Decoupled LiDAR Odometry
Queens Maria Thomas, Oliver Wasenmüller, Didier Stricker
Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion. In this paper, we pr…
An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching
René Schuster, Oliver Wasenmüller, Christian Unger +1
Training a deep neural network is a non-trivial task. Not only the tuning of hyperparameters, but also the gathering and selection of training data, the design of the loss function…
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation
Rohan Saxena, René Schuster, Oliver Wasenmüller +1
In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as…
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation
René Schuster, Oliver Wasenmüller, Christian Unger +2
State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense…