EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

11/09/2020
by   Hoang-An Le, et al.
16

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

07/31/2018

Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are va...
09/25/2019

Synthetic Data for Deep Learning

Synthetic data is an increasingly popular tool for training deep learnin...
07/19/2018

Three for one and one for three: Flow, Segmentation, and Surface Normals

Optical flow, semantic segmentation, and surface normals represent diffe...
08/19/2020

MineNav: An Expandable Synthetic Dataset Based on Minecraft for Aircraft Visual Navigation

We propose a simply method to generate high quality synthetic dataset ba...
08/12/2021

Memory-based Semantic Segmentation for Off-road Unstructured Natural Environments

With the availability of many datasets tailored for autonomous driving i...
04/16/2019

The ALOS Dataset for Advert Localization in Outdoor Scenes

The rapid increase in the number of online videos provides the marketing...
02/14/2022

COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets

Transfer learning is a proven technique in 2D computer vision to leverag...

Code Repositories