魚眼画像を用いた適応型軽量全方位ステレオ視のための幾何情報に基づく距離候補選択
Geometry-Informed Distance Candidate Selection for Adaptive Lightweight Omnidirectional Stereo Vision with Fisheye Images
魚眼カメラの全方位ステレオ距離推定において、幾何情報を利用して距離候補を絞り込み、計算コストを削減しつつ精度を向上させる手法を提案した。
著者: Conner Pulling, Je Hon Tan, Yaoyu Hu, Sebastian Scherer
分類: cs.CV, cs.RO
原文アブストラクト
Multi-view stereo omnidirectional distance estimation usually needs to build a cost volume with many hypothetical distance candidates. The cost volume building process is often computationally heavy considering the limited resources a mobile robot has. We propose a new geometry-informed way of distance candidates selection method which enables the use of a very small number of candidates and reduces the computational cost. We demonstrate the use of the geometry-informed candidates in a set of model variants. We find that by adjusting the candidates during robot deployment, our geometry-informed distance candidates also improve a pre-trained model's accuracy if the extrinsics or the number of cameras changes. Without any re-training or fine-tuning, our models outperform models trained with evenly distributed distance candidates. Models are also released as hardware-accelerated versions with a new dedicated large-scale dataset. The project page, code, and dataset can be found at https://theairlab.org/gicandidates/ .
