论文标题
伊甸园：封闭式花园场景的多模式合成数据集
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes
论文作者
论文摘要
用于室外场景的多模式大规模数据集主要是为城市驾驶问题而设计的。这些场景是高度结构化的，在语义上与以自然场景（例如花园或公园）中的情况不同。为了促进针对自然导向应用的机器学习方法，例如农业和园艺，我们提出了用于封闭花园场景（EDEN）的多模式合成数据集。该数据集具有从100多个花园型号捕获的30万张图像。每个图像都以各种低/高级视觉方式注释，包括语义分割，深度，表面正态，内在颜色和光流。关于语义分割和单眼深度预测的最新方法的实验结果，计算机视觉中的两个重要任务，在我们的数据集对非结构化自然场景上显示了预训练深网的积极影响。数据集和相关材料将在https://lhoangan.github.io/eden上找到。
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 https://lhoangan.github.io/eden.
