The majority of modern single-view depth estimation methods predict relative
depth and thus cannot be directly applied in many real-world scenarios, despite
impressive performance in the benchmarks. Moreover, single-view approaches
cannot guarantee consistency across a sequence of frames. Consistency is
typically addressed with test-time optimization of discrepancy across views;
however, it takes hours to process a single scene. In this paper, we present
MEDeA, an efficient multi-view test-time depth adjustment method, that is an
order of magnitude faster than existing test-time approaches. Given RGB frames
with camera parameters, MEDeA predicts initial depth maps, adjusts them by
optimizing local scaling coefficients, and outputs temporally-consistent depth
maps. Contrary to test-time methods requiring normals, optical flow, or
semantics estimation, MEDeA produces high-quality predictions with a depth
estimation network solely. Our method sets a new state-of-the-art on TUM RGB-D,
7Scenes, and ScanNet benchmarks and successfully handles smartphone-captured
data from ARKitScenes dataset.

该论文提出了一种高效的多视图测试时间深度调整方法 MEDeA，相比于现有的测试时间方法，它的处理速度快了一个数量级。通过利用 RGB 帧和相机参数，MEDeA 预测初始深度图，通过优化局部缩放系数来调整它们，并输出具有时间一致性的深度图。与需要法线、光流或语义估计的测试时间方法不同，MEDeA 仅通过深度估计网络就可以产生高质量的预测结果。该方法在 TUM RGB-D、7Scenes 和 ScanNet 基准测试中取得了最新的研究成果，并成功处理了 ARKitScenes 数据集中的智能手机捕获数据。

MEDeA：多视图高效深度调整

MEDeA: Multi-view Efficient Depth Adjustment

This letter introduces a novel framework for dense Visual Simultaneous
Localization and Mapping (VSLAM) based on Gaussian Splatting. Recently Gaussian
Splatting-based SLAM has yielded promising results, but rely on RGB-D input and
is weak in tracking. To address these limitations, we uniquely integrates
advanced sparse visual odometry with a dense Gaussian Splatting scene
representation for the first time, thereby eliminating the dependency on depth
maps typical of Gaussian Splatting-based SLAM systems and enhancing tracking
robustness. Here, the sparse visual odometry tracks camera poses in RGB stream,
while Gaussian Splatting handles map reconstruction. These components are
interconnected through a Multi-View Stereo (MVS) depth estimation network. And
we propose a depth smooth loss to reduce the negative effect of estimated depth
maps. Furthermore, the consistency in scale between the sparse visual odometry
and the dense Gaussian map is preserved by Sparse-Dense Adjustment Ring (SDAR).
We have evaluated our system across various synthetic and real-world datasets.
The accuracy of our pose estimation surpasses existing methods and achieves
state-of-the-art performance. Additionally, it outperforms previous monocular
methods in terms of novel view synthesis fidelity, matching the results of
neural SLAM systems that utilize RGB-D input.

基于高斯喷洒的稠密视觉同时定位与建图（VSLAM）引入了一种新的框架，通过独特地将先进的稀疏视觉里程计与稠密高斯喷洒场景表示相结合，从而消除了依赖于深度图的限制并增强了跟踪的稳健性。我们的系统在各种合成和真实数据集上的评估证明了其姿态估计的准确性超越了现有方法，达到了最先进的性能，并且在新颖视角合成保真度方面胜过以前的单目方法，与利用 RGB-D 输入的神经 SLAM 系统的结果相匹配。

MGS-SLAM: 单目稀疏跟踪与高斯建图并带深度平滑正则化

MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth  Smooth Regularization

With the frequent use of self-supervised monocular depth estimation in
robotics and autonomous driving, the model's efficiency is becoming
increasingly important. Most current approaches apply much larger and more
complex networks to improve the precision of depth estimation. Some researchers
incorporated Transformer into self-supervised monocular depth estimation to
achieve better performance. However, this method leads to high parameters and
high computation. We present a fully convolutional depth estimation network
using contextual feature fusion. Compared to UNet++ and HRNet, we use
high-resolution and low-resolution features to reserve information on small
targets and fast-moving objects instead of long-range fusion. We further
promote depth estimation results employing lightweight channel attention based
on convolution in the decoder stage. Our method reduces the parameters without
sacrificing accuracy. Experiments on the KITTI benchmark show that our method
can get better results than many large models, such as Monodepth2, with only 30
parameters. The source code is available at
this https URL

我们提出了一种全卷积深度估计网络，利用上下文特征融合，采用高分辨率和低分辨率特征来保留小目标和高速移动物体的信息，进一步在解码器阶段采用基于卷积的轻量级通道注意力来优化深度估计结果，降低参数数量同时保持准确性。