For the task of simultaneous monocular depth and visual odometry estimation,
we propose learning self-supervised transformer-based models in two steps. Our
first step consists in a generic pretraining to learn 3D geometry, using
cross-view completion objective (CroCo), followed by self-supervised finetuning
on non-annotated videos. We show that our self-supervised models can reach
state-of-the-art performance 'without bells and whistles' using standard
components such as visual transformers, dense prediction transformers and
adapters. We demonstrate the effectiveness of our proposed method by running
evaluations on six benchmark datasets, both static and dynamic, indoor and
outdoor, with synthetic and real images. For all datasets, our method
outperforms state-of-the-art methods, in particular for depth prediction task.

我们提出了一种使用自监督变换器模型进行单目深度和视觉里程计估计任务的方法，分为两个步骤：第一步是使用跨视角补全目标（CroCo）进行通用预训练以学习 3D 几何，然后在非标注视频上进行自监督微调。我们展示了我们的自监督模型可以通过使用视觉变换器、密集预测变换器和适配器等标准组件达到最先进的性能。通过在六个基准数据集上进行评估，包括静态和动态、室内和室外、合成和真实图像，我们证明了我们提出方法的有效性，尤其在深度预测任务中超过了最先进的方法。

自监督预训练和微调用于单目深度和视觉里程计

Self-supervised Pretraining and Finetuning for Monocular Depth and  Visual Odometry

We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for
highly accurate real-time visual odometry estimation of large-scale
environments from stereo cameras. It jointly optimizes for all the model
parameters within the active window, including the intrinsic/extrinsic camera
parameters of all keyframes and the depth values of all selected pixels. In
particular, we propose a novel approach to integrate constraints from static
stereo into the bundle adjustment pipeline of temporal multi-view stereo.
Real-time optimization is realized by sampling pixels uniformly from image
regions with sufficient intensity gradient. Fixed-baseline stereo resolves
scale drift. It also reduces the sensitivities to large optical flow and to
rolling shutter effect which are known shortcomings of direct image alignment
methods. Quantitative evaluation demonstrates that the proposed Stereo DSO
outperforms existing state-of-the-art visual odometry methods both in terms of
tracking accuracy and robustness. Moreover, our method delivers a more precise
metric 3D reconstruction than previous dense/semi-dense direct approaches while
providing a higher reconstruction density than feature-based methods.

提出了立体直接稀疏测距（Stereo DSO）作为一种新的方法，用于从立体相机中高精度实时估计大型环境的视觉测距，尤其是通过采样图像区域中具有足够强度梯度的像素实现实时优化和固定基线三维重建。