  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.

Computer Vision and Pattern Recognition