论文标题
闭塞输液：实时动态3D重建的闭塞感运动估计
OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction
论文作者
论文摘要
基于RGBD的实时动态3D重建遭受不准确的框架间运动估计，因为错误可能会通过在线跟踪累积。由于强烈的阻塞，对于基于单视图的系统而言，此问题更加严重。基于这些观察结果，我们提出了闭塞输液，这是一种计算闭塞感知3D运动以指导重建的新方法。在我们的技术中，首先估算可见区域的运动，并与时间信息结合使用，以通过LSTM涉及的图形神经网络推断被遮挡区域的运动。此外，我们的方法通过使用概率模型对网络输出进行建模来计算估计运动的置信度，从而减轻不信任的动作并实现强大的跟踪。公共数据集和我们自己记录的数据的实验结果表明，我们的技术的表现优于现有的基于单视图的实时方法。随着运动误差的减少，所提出的技术可以处理长而具有挑战性的运动序列。请查看项目页面以获取序列结果：https：//wenbin-lin.github.io/occlusionFusion。
RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate inter-frame motion estimation as errors may accumulate with online tracking. This problem is even more severe for single-view-based systems due to strong occlusions. Based on these observations, we propose OcclusionFusion, a novel method to calculate occlusion-aware 3D motion to guide the reconstruction. In our technique, the motion of visible regions is first estimated and combined with temporal information to infer the motion of the occluded regions through an LSTM-involved graph neural network. Furthermore, our method computes the confidence of the estimated motion by modeling the network output with a probabilistic model, which alleviates untrustworthy motions and enables robust tracking. Experimental results on public datasets and our own recorded data show that our technique outperforms existing single-view-based real-time methods by a large margin. With the reduction of the motion errors, the proposed technique can handle long and challenging motion sequences. Please check out the project page for sequence results: https://wenbin-lin.github.io/OcclusionFusion.
