WildUAV: Monocular UAV Dataset for Depth Estimation Tasks

Abstract

Aerial scene understanding systems face stringent payload restrictions and must often rely on monocular depth estimation for modelling scene geometry, which is an inherently ill-posed problem. Moreover, obtaining accurate ground truth data required by learning-based methods raises significant additional challenges in the aerial domain. Self-supervised approaches can bypass this problem, at the cost of providing only up-toscale results. Similarly, recent supervised solutions which make good progress towards zero-shot generalization also provide only relative depth values. This work presents TanDepth, a practical, online scale recovery method for obtaining metric depth results from relative estimations at inference-time, irrespective of the type of model generating them. Tailored for Unmanned Aerial Vehicle (UAV) applications, our method leverages sparse measurements from Global Digital Elevation Models (GDEM) by projecting them to the camera view using extrinsic and intrinsic information. An adaptation to the Cloth Simulation Filter is presented, which allows selecting ground points from the estimated depth map to then correlate with the projected reference points. We evaluate and compare our method against alternate scaling methods adapted for UAVs, on a variety of real-world scenes. Considering the limited availability of data for this domain, we construct and release a comprehensive, depth-focused extension to the popular UAVid dataset to further research.

Improved deep depth estimation for environments with sparse visual cues

“…The WildUAV dataset [12] is recorded in an outdoor area characterized by fields, trees and streets by an UAV with a camera mounted with an orthogonal view to the ground similar to the crane perspective. One main difference to the industry environment is the much larger distance toward objects which lies around 50 m on average.…”

“…As it is the first real data for that case, the results we observe are crucial to determine the direction of the research field of monocular depth in visual cue sparse environments. We conducted experiments by training different models on the artificial dataset StillBox [10] and the established indoor depth benchmark NYU Depth V2 [11], Furthermore, we benchmark our proposed model on the Wild-UAV dataset [12] which depicts bird's-eye view shots in rural outdoor environments and contains dense annotated depth to check the generalization abilities of our model.…”

A Self-Supervised Monocular Depth Estimation Approach Based on UAV Aerial Images

“…WildUAV [16] is employed as the training dataset. Wild-UAV raw dataset contains over 1500 high-resolution (5280 × 3956) images and their corresponding depth ground truth data is provided jointly with camera intrinsics.…”

“…5. Samples of the WildUAV dataset [16] Although WildUAV comprises high-resolution images with rich information, the total number of images is insufficient to avoid overfitting. Therefore, we pre-processed the images before training.…”

“…The model is trained on the low altitude UAVs dataset [16] , and experimental results are compared with other unsupervised models, showing a potential to make contributions to the safe operations of UAVs at low altitudes.…”