主题:SC-DepthV3-动态场景中的自监督单目深度估计 嘉宾:牛津大学博士后研究员 边佳旺 地点:TechBeat人工智能社区 Talk大纲 1、SC-Depth:解决视频中深度估计尺度不一致的问题 2、SC-DepthV2:解决室内场景中图像旋转造成的困难 3、SC-DepthV3:解决动态场景中动态物体造成的训练困难 Talk·预习资料 文章链接:https://a...
因此,SC-Depth系列作者又在近日提出了SC-DepthV3,面向高动态场景的单目深度估计网络!在各种动态场景中都可以鲁棒的运行!具体来说,SC-DepthV3首先引入了一个在大规模数据集上有监督预训练的单目深度估计模型LeReS,并通过零样本泛化提供单图像深度先验,也就是伪深度,同时引入了一个新损失来约束网络的训练。注意,LeReS...
因此,SC-Depth系列作者又在近日提出了SC-DepthV3,面向高动态场景的单目深度估计网络! 在各种动态场景中都可以鲁棒的运行! 具体来说,SC-DepthV3首先引入了一个在大规模数据集上有监督预训练的单目深度估计模型LeReS,并通过零样本泛化提供单图像深度先验,也就是伪深度,同时引入了一个新损失来约束网络的训练。 注意,...
定量结果显示,SC-DepthV1取得了与Monodepth2相持平的结果。但SC-DepthV1估计出的深度图具有连续性,因此还是SC-DepthV1更胜一筹。 SCDepthV3 SC-DepthV1面向室外场景,SC-DepthV2面向室内场景,可以说实现了很好的通用性和泛化能力。但SC-DepthV1和SC-DepthV2都是基于静态环境假设的,虽然作者也利用Mask剔除了一些...
SC_Depth: This repo provides the pytorch lightning implementation of SC-Depth (V1, V2, and V3) for self-supervised learning of monocular depth from video. In the SC-DepthV1 (IJCV 2021 & NeurIPS 2019), we propose (i) geometry consistency loss for scale-consistent depth prediction over time...
In the SC-DepthV3 (ArXiv 2022), we propose a robust learning framework for accurate and sharp monocular depth estimation in (highly) dynamic scenes. As the photometric loss, which is the main loss in the self-supervised methods, is not valid in dynamic object regions and occlusion, previous...
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In the SC-DepthV3 (ArXiv 2022), we propose a robust learning framework for accurate and sharp monocular depth estimation in (highly) dynamic scenes. As the photometric loss, which is the main loss in the self-supervised methods, is not valid in dynamic object regions and occlusion, previous...