SAILenv: Learning in Virtual Visual Environments Made Simple
Enrico Meloni, Luca Pasqualini, Matteo Tiezzi, Marco Gori, Stefano, Melacci

TL;DR
SAILenv is a user-friendly, customizable virtual 3D environment platform designed for visual recognition research, offering photorealistic scenes, pixel-level labeling, and motion data to facilitate algorithm testing.
Contribution
The paper introduces SAILenv, a novel, easy-to-use platform that provides photorealistic virtual environments with motion information, simplifying the testing of visual recognition algorithms.
Findings
Object detector trained on real images recognizes 3D objects in SAILenv.
Optical flow computation in SAILenv is efficient and GPU-compatible.
Platform enables realistic testing with minimal code and customization.
Abstract
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world. However, most of the existing platforms to interface algorithms with 3D environments are often designed to setup navigation-related experiments, to study physical interactions, or to handle ad-hoc cases that are not thought to be customized, sometimes lacking a strong photorealistic appearance and an easy-to-use software interface. In this paper, we present a novel platform, SAILenv, that is specifically designed to be simple and customizable, and that allows researchers to experiment visual recognition in virtual 3D scenes. A few lines of code are needed to interface every algorithm with the virtual world, and non-3D-graphics experts can…
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Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Neural Network Applications · Advanced Image and Video Retrieval Techniques
