RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds

no code implementations • 1 Apr 2022 • Ramy Battrawy, René Schuster, Mohammad-Ali Nikouei Mahani, Didier Stricker

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.

Scene Flow Estimation

DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR

no code implementations • 18 Aug 2020 • Rishav, Ramy Battrawy, René Schuster, Oliver Wasenmüller, Didier Stricker

In this paper, we present DeepLiDARFlow, a novel deep learning architecture which fuses high level RGB and LiDAR features at multiple scales in a monocular setup to predict dense scene flow.

3D Reconstruction Scene Flow Estimation

ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching

no code implementations • 22 Jun 2020 • Rishav, René Schuster, Ramy Battrawy, Oliver Wasenmüller, Didier Stricker

Thus, we present ResFPN -- a multi-resolution feature pyramid network with multiple residual skip connections, where at any scale, we leverage the information from higher resolution maps for stronger and better localized features.

Optical Flow Estimation Scene Flow Estimation

LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images

no code implementations • 31 Oct 2019 • Ramy Battrawy, René Schuster, Oliver Wasenmüller, Qing Rao, Didier Stricker

We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images.

Scene Flow Estimation