Vanishing point estimation inspired by oblique effect in a field environment.

Estimating a vanishing point (VP) is a core problem for understanding three-dimensional scenes and autonomous navigation. Existing methods are essential to estimating VPs in indoor and urban environments. However, doing so in diverse, unstructured, changing, and unexpected field environments remains a considerable challenge. Traditional methods of estimating structural VP have some shortcomings as they rely heavily on feature-intensive computation, making them less reliable due to a lack of adequate structures in a field environment due to disorganized disturbances. Inspired by the oblique effect, neurons prefer to respond to horizontal and vertical stimuli more than to diagonal, which can help estimate VPs. This study proposes a methodology to estimate VPs from a monocular camera for a field environment. Local orientation features are assigned to clusters inspired by the oblique effect. By extracting end points of different clusters, virtual local orientation features are reshaped. Based on geometric inferences of orientation, a VP is approximately estimated using optimal estimation and self-selectability. No prior training is needed, and camera calibration and internal parameters are not required. This approach is robust to changes in color and illumination using geometric inference, making it a perfect fit for field environments. Experimental results demonstrated that the method can successfully estimate VPs. This study presents a groundbreaking approach to evaluating VPs using a monocular camera. Inspired by the oblique effect, our method relies on explainable geometric inferences instead of prior training, resulting in a highly robust model that can handle changes in color and illumination. Our proposed approach significantly advances scene understanding and navigation, making it an ideal solution for field environments.