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 this https URL .

使用几何信息的候选距离方法可以有效减少计算成本，在多视角立体全向距离估计中取得更好的结果。