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Multimodal large-scale datasets for outdoor scenes are mostly designed forurban driving problems. The scenes are highly structured and semanticallydifferent from scenarios seen in nature-centered scenes such as gardens orparks. To promote machine learning methods for nature-oriented applications,such as agriculture and gardening, we propose the multimodal synthetic datasetfor Enclosed garDEN scenes (EDEN). The dataset features more than 300K imagescaptured from more than 100 garden models. Each image is annotated with variouslow/high-level vision modalities, including semantic segmentation, depth,surface normals, intrinsic colors, and optical flow. Experimental results onthe state-of-the-art methods for semantic segmentation and monocular depthprediction, two important tasks in computer vision, show positive impact ofpre-training deep networks on our dataset for unstructured natural scenes. Thedataset and related materials will be available athttps://lhoangan.github.io/eden.

EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes