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伊甸园:封闭花园场景的多模态合成数据集(CS CV)

2020-12-07 19:23:36 凌茜

室外场景的多模态大尺度数据集大多是针对城市驾驶问题而设计的。这些场景是高度结构化的,语义上不同于在以自然为中心的场景中看到的场景,如花园或公园。为了促进机器学习方法用于农业和园艺等面向自然的应用,我们提出了封闭花园场景的多模态合成数据集(EDEN)。该数据集包含从100多个花园模型捕获的超过30万幅图像。每幅图像都用各种低/高视觉模式进行注释,包括语义分割、深度、表面法线、内在颜色和光流。语义分割和单目深度预测这两个计算机视觉中的重要任务的最新方法的实验结果表明,训练前的深度网络对我们的非结构化自然场景数据集有积极的影响。

原文题目:EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

原文:Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems. The scenes are highly structured and semantically different from scenarios seen in nature-centered scenes such as gardens or parks. To promote machine learning methods for nature-oriented applications, such as agriculture and gardening, we propose the multimodal synthetic dataset for Enclosed garDEN scenes (EDEN). The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow. Experimental results on the state-of-the-art methods for semantic segmentation and monocular depth prediction, two important tasks in computer vision, show positive impact of pre-training deep networks on our dataset for unstructured natural scenes. The dataset and related materials will be available at this https URL.

原文作者:Hoang-An Le, Thomas Mensink, Partha Das, Sezer Karaoglu, Theo Gevers

原文地址:https://arxiv.org/abs/2011.04389

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https://cloud.tencent.com/developer/article/1748788