Geometry-Informed Distance Candidate Selection for Adaptive Lightweight Omnidirectional Stereo Vision with Fisheye Images
This work addresses the problem of efficient distance estimation for mobile robots with limited resources, offering an incremental improvement in lightweight stereo vision.
The paper tackles the computational burden of multi-view stereo omnidirectional distance estimation by proposing a geometry-informed distance candidate selection method that reduces the number of candidates needed, thereby lowering computational costs, and it shows that this approach improves model accuracy without retraining when camera configurations change, outperforming models with evenly distributed candidates.
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/ .