都市規模の高密度3次元復元を効率的に行うシステム
DENSER Cities: A System for Dense Efficient Reconstructions of Cities
ステレオカメラから都市規模の高密度カラー3次元モデルを効率的に生成するため、圧縮3Dデータ構造に正則化を適用する手法を提案し、KITTIで精度6cm・誤差40%削減を達成した。
著者: Michael Tanner, Pedro Pinies, Lina Maria Paz, Paul Newman
分類: cs.CV, cs.RO
原文アブストラクト
This paper is about the efficient generation of dense, colored models of city-scale environments from range data and in particular, stereo cameras. Better maps make for better understanding; better understanding leads to better robots, but this comes at a cost. The computational and memory requirements of large dense models can be prohibitive. We provide the theory and the system needed to create city-scale dense reconstructions. To do so, we apply a regularizer over a compressed 3D data structure while dealing with the complex boundary conditions this induces during the data-fusion stage. We show that only with these considerations can we swiftly create neat, large, "well behaved" reconstructions. We evaluate our system using the KITTI dataset and provide statistics for the metric errors in all surfaces created compared to those measured with 3D laser. Our regularizer reduces the median error by 40% in 3.4 km of dense reconstructions with a median accuracy of 6 cm. For subjective analysis, we provide a qualitative review of 6.1 km of our dense reconstructions in an attached video. These are the largest dense reconstructions from a single passive camera we are aware of in the literature.
