Color-guided depth super-resolution (DSR) is an encouraging paradigm that
enhances a low-resolution (LR) depth map guided by an extra high-resolution
(HR) RGB image from the same scene. Existing methods usually use interpolation
to upscale the depth maps before feeding them into the network and transfer the
high-frequency information extracted from HR RGB images to guide the
reconstruction of depth maps. However, the extracted high-frequency information
usually contains textures that are not present in depth maps in the existence
of the cross-modality gap, and the noises would be further aggravated by
interpolation due to the resolution gap between the RGB and depth images. To
tackle these challenges, we propose a novel Symmetric Uncertainty-aware Feature
Transmission (SUFT) for color-guided DSR. (1) For the resolution gap, SUFT
builds an iterative up-and-down sampling pipeline, which makes depth features
and RGB features spatially consistent while suppressing noise amplification and
blurring by replacing common interpolated pre-upsampling. (2) For the
cross-modality gap, we propose a novel Symmetric Uncertainty scheme to remove
parts of RGB information harmful to the recovery of HR depth maps. Extensive
experiments on benchmark datasets and challenging real-world settings suggest
that our method achieves superior performance compared to state-of-the-art
methods. Our code and models are available at
this https URL

本文提出了一种新颖的对称不确定性特征传输（SUFT）来解决深度图超分辨率中颜色指导的挑战，包括分辨率差异和跨模态差异，并取得了优于现有方法的表现。

深度超分辨率对称不确定性感知特征传输

Symmetric Uncertainty-Aware Feature Transmission for Depth  Super-Resolution

We present a learning-based method for 6 DoF pose estimation of rigid objects
in point cloud data. Many recent learning-based approaches use primarily RGB
information for detecting objects, in some cases with an added refinement step
using depth data. Our method consumes unordered point sets with/without RGB
information, from initial detection to the final transformation estimation
stage. This allows us to achieve accurate pose estimates, in some cases
surpassing state of the art methods trained on the same data.

本文提出了一种基于学习的方法，用于在点云数据中完成刚性物体的 6 自由度姿态估计，并且相比于使用 RGB 信息进行物体检测的方法，本方法可以通过初始检测到最终转换估计阶段处理无序的点集来实现准确的姿态估计，有些情况下还能够超过在相同数据上训练的最先进的方法.