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...
在具体的评估上,SC-DepthV3在DDAD、BONN、TUM、KITTI、NYUv2和IBims-1这六个数据集进行了大量实验,定性结果显示SC-DepthV3在动态环境中具有极强的鲁棒性。定量结果也说明了SC-DepthV3在动态环境中的性能远超Monodepth2和SC-Depth。 一句话总结:SC-DepthV3解决了动态环境问题。 5. 总结 SC-Depth系列是非常经典...
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,...
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剔除了一些动态物体的影响,但当应用场景是高动态环境时,算法很容易崩溃...
It is integrated into SC-DepthV1 and jointly trained with self-supervised losses, greatly boosting the performance. 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, ...
SC_Depth: This repo provides the pytorch lightning implementation ofSC-Depth(V1, V2, and V3) forself-supervised learning of monocular depth from video. In the SC-DepthV1 (IJCV 2021&NeurIPS 2019), we propose (i)geometry consistency lossfor scale-consistent depth prediction over time and (ii...
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Is there enough left in the AirCheck to hope for a v3? I’d love to see a cleaner picture about link-live and how it plays a role in the beloved AirCheck G2. I’d love to hear a definitive story on the likes of AirMagnet Survey Pro, Wi-Fi Analyzer, Spectrum XT – all of ...
Many of us in the industry love our hulk green Wi-Fi diagnostics tool andthe G2 v2 additions were welcome. Is there enough left in the AirCheck to hope for a v3? I’d love to see a cleaner picture about link-live and how it plays a role in the beloved AirCheck G2. I’d love...
SC-Depth (V1, V2, and V3) for Unsupervised Monocular Depth Estimation Webpage:https://jiawangbian.github.io/sc_depth_pl/ - cudnn/sc_depth_pl