Omnidata：3Dスキャンからマルチタスク中間視覚データセットを生成するスケーラブルなパイプライン
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
実世界の3Dスキャンをパラメトリックにサンプリング・レンダリングして多タスク視覚データセットを生成するパイプラインを提案し、生成データのみで学習したモデルが複数の視覚ベンチマークで最先端性能を達成した。
著者: Ainaz Eftekhar, Alexander Sax, Roman Bachmann, Jitendra Malik, Amir Zamir
分類: cs.CV, cs.AI, cs.GR, cs.RO
原文アブストラクト
This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world. Changing the sampling parameters allows one to "steer" the generated datasets to emphasize specific information. In addition to enabling interesting lines of research, we show the tooling and generated data suffice to train robust vision models. Common architectures trained on a generated starter dataset reached state-of-the-art performance on multiple common vision tasks and benchmarks, despite having seen no benchmark or non-pipeline data. The depth estimation network outperforms MiDaS and the surface normal estimation network is the first to achieve human-level performance for in-the-wild surface normal estimation -- at least according to one metric on the OASIS benchmark. The Dockerized pipeline with CLI, the (mostly python) code, PyTorch dataloaders for the generated data, the generated starter dataset, download scripts and other utilities are available through our project website, https://omnidata.vision.
