PedX: 複雑な都市交差点における歩行者のメートル単位3D姿勢推定のためのベンチマークデータセット
PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections
都市交差点の歩行者を対象に、高解像度ステレオ画像とLiDAR、2D/3Dラベルを備えた大規模マルチモーダルデータセットPedXを構築し、異なるモダリティの制約と形状・時間的事前分布を活用した自動3Dラベリング手法を提案した論文。
著者: Wonhui Kim, Manikandasriram Srinivasan Ramanagopal, Charles Barto, Ming-Yuan Yu, Karl Rosaen, Nick Goumas, Ram Vasudevan, Matthew Johnson-Roberson
分類: cs.CV, cs.RO
原文アブストラクト
This paper presents a novel dataset titled PedX, a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5,000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing 2D and 3D labels of pedestrians. We also present a novel 3D model fitting algorithm for automatic 3D labeling harnessing constraints across different modalities and novel shape and temporal priors. All annotated 3D pedestrians are localized into the real-world metric space, and the generated 3D models are validated using a mocap system configured in a controlled outdoor environment to simulate pedestrians in urban intersections. We also show that the manual 2D labels can be replaced by state-of-the-art automated labeling approaches, thereby facilitating automatic generation of large scale datasets.
