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Nov 10, 2023 · Abstract:Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for ...
Feb 20, 2023 · In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity ...
Our self-supervised model,. Monodepth2, produces sharp, high quality depth maps, whether trained with monocular (M), stereo (S), or joint (MS) supervision.
Abstract. Self-supervised monocular depth estimation presents a pow- erful method to obtain 3D scene information from single camera images,.
We introduce ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. ✓ Self- ...
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May 12, 2021 · In monocular depth estimation, the goal is the generation of pixel-wise estimates (a.k.a. a depth map) of how far each scene element is from the ...
The RoboDepth Challenge Team is evaluating the robustness of different depth estimation algorithms. MonoViT has achieved the outstanding robustness. KITTI ...
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser ...
In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self- ...
Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image.