论文标题
深度灌注：使用单图像相对深度预测的致密单眼猛击
DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction
论文作者
论文摘要
在本文中，我们提出了一个致密的单眼大满贯系统，称为DeepRelativeFusion，该系统能够恢复全球一致的3D结构。为此，我们使用视觉大满贯算法可靠地恢复关键框架的相机姿势和半密度的深度图，然后使用相对深度预测来使半密度的深度图保持密集，并完善密钥框架姿势。为了改善半密度的深度图，我们提出了一种自适应过滤方案，该方案是一种具有结构的加权平滑过滤器，考虑到相邻像素的像素强度和深度，从而在致密化中产生了实质性的重建精度增益。为了执行致密化，我们在深入灌注提出的能量最小化框架上引入了两个增量改进：（1）改善的成本函数，以及（2）使用单像相对深度预测。致密化后，我们使用两视图一致的优化半密度和密集的深度图更新关键帧，以改善姿势图优化，从而提供反馈循环，以完善钥匙帧的姿势，以进行准确的场景重建。我们的系统的表现要优于最先进的密集大满贯系统，以大量的重建精度，较大的边距。
In this paper, we propose a dense monocular SLAM system, named DeepRelativeFusion, that is capable to recover a globally consistent 3D structure. To this end, we use a visual SLAM algorithm to reliably recover the camera poses and semi-dense depth maps of the keyframes, and then use relative depth prediction to densify the semi-dense depth maps and refine the keyframe pose-graph. To improve the semi-dense depth maps, we propose an adaptive filtering scheme, which is a structure-preserving weighted average smoothing filter that takes into account the pixel intensity and depth of the neighbouring pixels, yielding substantial reconstruction accuracy gain in densification. To perform densification, we introduce two incremental improvements upon the energy minimization framework proposed by DeepFusion: (1) an improved cost function, and (2) the use of single-image relative depth prediction. After densification, we update the keyframes with two-view consistent optimized semi-dense and dense depth maps to improve pose-graph optimization, providing a feedback loop to refine the keyframe poses for accurate scene reconstruction. Our system outperforms the state-of-the-art dense SLAM systems quantitatively in dense reconstruction accuracy by a large margin.
