WayveScenes101: 自動運転における新規視点合成のためのデータセットとベンチマーク
WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving
動的な要素や厳しい環境条件を含む101の実走行シーンを集め、訓練視点から外れた視点での新規視点合成性能を評価するデータセットとベンチマークを提案した。
著者: Jannik Zürn, Paul Gladkov, Sofía Dudas, Fergal Cotter, Sofi Toteva, Jamie Shotton, Vasiliki Simaiaki, Nikhil Mohan
分類: cs.CV, cs.GR, cs.RO
原文アブストラクト
We present WayveScenes101, a dataset designed to help the community advance the state of the art in novel view synthesis that focuses on challenging driving scenes containing many dynamic and deformable elements with changing geometry and texture. The dataset comprises 101 driving scenes across a wide range of environmental conditions and driving scenarios. The dataset is designed for benchmarking reconstructions on in-the-wild driving scenes, with many inherent challenges for scene reconstruction methods including image glare, rapid exposure changes, and highly dynamic scenes with significant occlusion. Along with the raw images, we include COLMAP-derived camera poses in standard data formats. We propose an evaluation protocol for evaluating models on held-out camera views that are off-axis from the training views, specifically testing the generalisation capabilities of methods. Finally, we provide detailed metadata for all scenes, including weather, time of day, and traffic conditions, to allow for a detailed model performance breakdown across scene characteristics. Dataset and code are available at https://github.com/wayveai/wayve_scenes.
