EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

TL;DR

EDEN is a large-scale synthetic multimodal dataset of enclosed garden scenes designed to advance machine learning for nature-oriented applications like gardening and agriculture, with diverse annotations and demonstrated benefits for vision tasks.

Contribution

The paper introduces EDEN, a novel synthetic dataset of garden scenes with multimodal annotations, filling a gap in outdoor scene datasets for natural environments.

Findings

Pre-training on EDEN improves semantic segmentation accuracy.

Pre-training on EDEN enhances monocular depth prediction performance.

The dataset supports various vision tasks with positive experimental results.

Abstract

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.