Depth estimation in surgical video plays a crucial role in many image-guided
surgery procedures. However, it is difficult and time consuming to create depth
map ground truth datasets in surgical videos due in part to inconsistent
brightness and noise in the surgical scene. Therefore, building an accurate and
robust self-supervised depth and camera ego-motion estimation system is gaining
more attention from the computer vision community. Although several
self-supervision methods alleviate the need for ground truth depth maps and
poses, they still need known camera intrinsic parameters, which are often
missing or not recorded. Moreover, the camera intrinsic prediction methods in
existing works depend heavily on the quality of datasets. In this work, we
aimed to build a self-supervised depth and ego-motion estimation system which
can predict not only accurate depth maps and camera pose, but also camera
intrinsic parameters. We proposed a cost-volume-based supervision manner to
give the system auxiliary supervision for camera parameters prediction. The
experimental results showed that the proposed method improved the accuracy of
estimated camera parameters, ego-motion, and depth estimation.

建立了一种自监督深度和相机自运动估计系统，能够预测准确的深度图、相机姿态和相机内部参数，并通过基于成本体积的监督方式提高了估计的准确性。

WS-SfMLearner: 自监督单目深度和自我运动估计：用于未知摄像机参数的外科手术视频

WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation  on Surgical Videos with Unknown Camera Parameters

In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute
human pose estimation with calibrated camera. Accurate and generalizable
absolute 3D human pose estimation from monocular 2D pose input is an ill-posed
problem. To address this challenge, we convert the input from pixel space to 3D
normalized rays. This conversion makes our approach robust to camera intrinsic
parameter changes. To deal with the in-the-wild camera extrinsic parameter
variations, Ray3D explicitly takes the camera extrinsic parameters as an input
and jointly models the distribution between the 3D pose rays and camera
extrinsic parameters. This novel network design is the key to the outstanding
generalizability of Ray3D approach. To have a comprehensive understanding of
how the camera intrinsic and extrinsic parameter variations affect the accuracy
of absolute 3D key-point localization, we conduct in-depth systematic
experiments on three single person 3D benchmarks as well as one synthetic
benchmark. These experiments demonstrate that our method significantly
outperforms existing state-of-the-art models. Our code and the synthetic
dataset are available at this https URL .

本文提出了一种基于光线的单目 3D (Ray3D) 绝对人体姿态估计方法，通过将输入数据从像素空间转换为三维规范化光线并明确将相机外参作为输入，该方法具有优异的泛化性能，并能显著超越现有的同类模型。