自動運転知覚のための高精度合成画像・LiDARデータセットPreSIL
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
ゲームGTA V内に精密なLiDARシミュレータを構築し、5万フレーム超の画像・深度・セグメンテーション・注釈付きデータセットを自動生成。事前学習に用いることでKITTI 3D物体検出の精度が最大5%向上した。
著者: Braden Hurl, Krzysztof Czarnecki, Steven Waslander
分類: cs.CV, cs.RO
原文アブストラクト
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at https://tinyurl.com/y3tb9sxy
