26 citations · 71 across the 39 of their papers we have counts for
5 papers · 1 filter
Automated Scene Flow Data Generation for Training and Verification
Oliver Wasenmüller, René Schuster, Didier Stricker +6
Scene flow describes the 3D position as well as the 3D motion of each pixel in an image. Such algorithms are the basis for many state-of-the-art autonomous or automated driving fun…
Dense Scene Flow from Stereo Disparity and Optical Flow
René Schuster, Oliver Wasenmüller, Didier Stricker
Scene flow describes 3D motion in a 3D scene. It can either be modeled as a single task, or it can be reconstructed from the auxiliary tasks of stereo depth and optical flow estima…
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning
Patrik Feth, Mohammed Naveed Akram, René Schuster +1
Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driv…
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation
René Schuster, Christian Bailer, Oliver Wasenmüller +1
Optical Flow algorithms are of high importance for many applications. Recently, the Flow Field algorithm and its modifications have shown remarkable results, as they have been eval…
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
René Schuster, Christian Bailer, Oliver Wasenmüller +1
Scene flow is a description of real world motion in 3D that contains more information than optical flow. Because of its complexity there exists no applicable variant for real-time…