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

Author:

Battrawy Ramy1,Schuster Rene1,Mahani Mohammad-Ali Nikouei2,Stricker Didier1

Affiliation:

1. DFKI - German Research Center for Artificial Intelligence,Germany

2. BMW Group,Germany

Publisher

IEEE

Reference36 articles.

1. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

2. PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

3. RAFT: Recurrent all-pairs field transforms for optical flow;teed;European Conference on Computer Vision (ECCV),0

4. Optimal Trans-port for structured data with application on graphs;titouan;International Conference on Machine Learning,0

5. Object scene flow for autonomous ve-hicles;menze;IEEE International Conference on Computer Vision and Pattern Recognition (CVPR),0

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RMS-FlowNet++: Efficient and Robust Multi-scale Scene Flow Estimation for Large-Scale Point Clouds;International Journal of Computer Vision;2024-05-23

2. Multi-Body Neural Scene Flow;2024 International Conference on 3D Vision (3DV);2024-03-18

3. Deep Scene Flow Learning: From 2D Images to 3D Point Clouds;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-01

4. FlowFormer: 3D scene flow estimation for point clouds with transformers;Knowledge-Based Systems;2023-11

5. IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow Estimation;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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