RAFT论文笔记 (论文解读)RAFT: Recurrent All-Pairs Field Transforms for Optical Flow 《RAFT:Recurrent All-Pairs Field Transforms for Optical Flow》论文笔记 github.com/princeton-vl hci-benchmark.iwr.uni-heidelberg.de(HD1K数据集) 编辑于 2022-01-03 22:07 ...
We introduce SEA-RAFT, a more simple, efficient, and accurateRAFTfor optical flow. Compared with RAFT, SEA-RAFT is trained with a new loss (mixture of Laplace). It directly regresses an initial flow for faster convergence in iterative refinements and introduces rigid-motion pre-training to impr...
EzFlow: A modular PyTorch library for optical flow estimation using neural networks. 2021. Web. https://github.com/neu-vi/ezflow. [3] Greff, Klaus, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet et al. Kubric: A scalable dataset generator. In ...
由于其高效性,SEA-RAFT的运行速度至少比现有方法快2.3倍,同时保持竞争性能。代码在https://github.com/princeton-vl/SEA-RAFT公开。 3. 效果展示 - 准确性:在Spring数据集上,SEA-RAFT实现了一个新的最优性能,明显优于次优性能:1像素异常点错误率减少18%(3.686 vs. 4.482),端点误差减少24%(0.363 vs. 0.471)...
此模型称为 RAFT:Recurrent All-Pairs Field Transforms for Optical Flow,在 PyTorch 或 GitHub 上很容易获得。这些实现使其具有高度可访问性,但模型很复杂,理解它可能会令人困惑。在这篇文章中,我们将把 RAFT 分解成它的基本组件,并详细了解它们中的每一个。然后我们将学习如何在 Python 中使用它来估计光流。在...
We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D correlation volumes for all pairs of pixels, and iteratively updates a flow field through a recurrent unit that performs lookups on ...
RAFT: Recurrent All Pairs Field Transforms for Optical Flow ECCV 2020 Zachary Teed and Jia Deng Requirements The code has been tested with PyTorch 1.6 and Cuda 10.1. conda create --name raft conda activate raft conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard...
本项目复现了光流估计的经典模型RAFT,该模型出自ECCV2020年的优秀论文《Recurrent All-Pairs Field Transforms for Optical Flow》。由于AI Studio开源项目和数据集中鲜有涉及光流估计领域,为了让大家更好地理解该领域,本项目从光流估计的基础入手,先对数据集、评价指标进行介绍,然后再介绍RAFT模型。
代码库:https://github.com/USTC-AIS-Lab/RAFT-VINS/tree/main 1. 寻找基础镜像 https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorrt/layers 这里我们查看 tensorr
C+T+SSelfFlow[26]--3.744.26 Ours--2.773.61 Ours (warm-start)--2.423.39 Table 2: Results onKITTI. When trained only on synthetic data (C+T), RAFT generalizes well onKITTIespecially when compared to other deep networks usingEPE. After finetuning onKITTI, we match theperformanceof VCN ...