Temporal-controlled Frame Swap for Generating High-Fidelity Stereo Driving Data for Autonomy Analysis

TL;DR

This paper introduces TeFS, a novel method for generating high-quality synthetic stereo driving data from GTA V, enabling improved evaluation and development of vSLAM models for autonomous driving.

Contribution

The paper presents TeFS, a new technique for creating large-scale, high-fidelity stereo datasets from commercial game engines, filling a gap in available training data for vSLAM research.

Findings

GTAV-TeFS dataset contains over 88,000 stereo image pairs with rich metadata.

TeFS-generated data is validated against conventional dual-viewport data.

Benchmarking reveals strengths and limitations of current vSLAM models.

Abstract

This paper presents a novel approach, TeFS (Temporal-controlled Frame Swap), to generate synthetic stereo driving data for visual simultaneous localization and mapping (vSLAM) tasks. TeFS is designed to overcome the lack of native stereo vision support in commercial driving simulators, and we demonstrate its effectiveness using Grand Theft Auto V (GTA V), a high-budget open-world video game engine. We introduce GTAV-TeFS, the first large-scale GTA V stereo-driving dataset, containing over 88,000 high-resolution stereo RGB image pairs, along with temporal information, GPS coordinates, camera poses, and full-resolution dense depth maps. GTAV-TeFS offers several advantages over other synthetic stereo datasets and enables the evaluation and enhancement of state-of-the-art stereo vSLAM models under GTA V's environment. We validate the quality of the stereo data collected using TeFS by conducting a comparative analysis with the conventional dual-viewport data using an open-source simulator. We also benchmark various vSLAM models using the challenging-case comparison groups included in GTAV-TeFS, revealing the distinct advantages and limitations inherent to each model. The goal of our work is to bring more high-fidelity stereo data from commercial-grade game simulators into the research domain and push the boundary of vSLAM models.