We present a novel unsupervised learning framework for single view depth estimation using monocular videos. It is well known in 3D vision that enlarging the baseline can increase the depth estimation accuracy, and jointly optimizing a set of camera poses and landmarks is essential. In previous monocular unsupervised learning frameworks, only part of the photometric and geometric constraints within a sequence are used as supervisory signals. This may result in a short baseline and overfitting. Besides, previous works generally estimate a low resolution depth from a low resolution impute image. The low resolution depth is then interpolated to recover the original resolution. This strategy may generate large errors on object boundaries, as the depth of background and foreground are mixed to yield the high resolution depth. In this paper, we introduce a bundle adjustment framework and a super-resolution network to solve the above two problems. In bundle adjustment, depths and poses of an image sequence are jointly optimized, which increases the baseline by establishing the relationship between farther frames. The super resolution network learns to estimate a high resolution depth from a low resolution image. Additionally, we introduce the clip loss to deal with moving objects and occlusion. Experimental results on the KITTI dataset show that the proposed algorithm outperforms the state-of-the-art unsupervised methods using monocular sequences, and achieves comparable or even better result compared to unsupervised methods using stereo sequences.
Lightweight Prompt-Guided CLIP Adaptation for Monocular Depth Estimation
School of Engineering Science, Simon Fraser University（西蒙弗雷泽大学工程科学学院）；School of Information Science and Technology, Eastern Institute of Technology（东方理工学院信息科学与技术学院）
Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation
MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能 MOE 实验室，上海交通大学人工智能学院）；Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China（宁波数字孪生研究所，东技术研究所，宁波，中国）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Ningbo, China（宁波空间智能与数字衍生关键实验室，宁波，中国）；University of Science and Technology of China（中国科学技术大学）；CASIA；Tsinghua University（清华大学）
School of Computer Science, The University of Adelaide（阿德莱德大学计算机科学学院）；School of Electrical Engineering and Computer Science, Queensland University of Technology（昆士兰科技大学电气与计算机科学学院）
HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields
University of Stuttgart（斯图加特大学）；Huawei Munich Research Center（华为慕尼黑研究中心）；Central Media Technology Institute, Huawei 2012 Laboratories（华为2012实验室中央媒体技术研究院）；Technical University of Munich（慕尼黑工业大学）
Language as Prior, Vision as Calibration: Metric Scale Recovery for Monocular Depth Estimation
Hefei University of Technology, Hefei, Anhui, China（合肥工业大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China（人工智能研究所）；University of Science and Technology of China, Hefei, Anhui, China（中国科学技术大学）
SCE-SLAM: Scale-Consistent Monocular SLAM via Scene Coordinate Embeddings
School of Computer Science and Engineering, State Key Laboratory of Complex & Critical Software Environment, Jiangxi Research Institute, Beihang University（计算机科学与工程学院、复杂与关键软件环境国家重点实验室、江西研究院、北航）；Macquarie University（麦考瑞大学）；Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology（北京半导体神经网络智能感知与计算技术重点实验室）
HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction
Institute for Photogrammetry and Geoinformatics, University of Stuttgart（摄影测量与地理信息学研究所，斯图加特大学）；Technical University of Munich（慕尼黑技术大学）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
MagicHOI: Leveraging 3D Priors for Accurate Hand-object Reconstruction from Short Monocular Video Clips
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）
RoSe-SLAM: Robust Semantic-Aware Gaussian Splatting SLAM from Dynamic Monocular Videos
The Chinese University of Hong Kong（香港中文大学）；The University of Manchester（曼彻斯特大学）；Centre for Perceptual and Interactive Intelligence (CPII) Limited（感知与互动智能中心有限公司）
Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Shanghai Institute of Intelligent Science and Technology（上海智能科学技术研究院）；State Key Laboratory of Intelligent Autonomous Systems（智能自主系统国家重点实验室）；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）
Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes
Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University（上海交通大学医学机器人研究所和自动化系）；Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai（系统控制与信息处理重点实验室，教育部，上海）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
Center for Robotics, University of Bonn（博恩大学机器人中心）；MAVLab, TU Delft（代尔夫特理工大学MAVLab）；Lamarr Institute for Machine Learning and Artificial Intelligence（兰玛尔人工智能与机器学习研究所）
MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping
Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系）；Department of Mechanical and Process Engineering, ETH Zurich（苏黎世联邦理工学院机械与工艺工程系）；Department of Informatics, University of Zurich（苏黎世大学信息系）；Institute for Photogrammetry, University of Stuttgart（斯图加特大学摄影测量研究所）；Computer Vision and Geometry Group, ETH Zurich（苏黎世联邦理工学院计算机视觉与几何组）；Microsoft Spatial AI Lab（微软空间AI实验室）；Computer Vision Research Group, University of Amsterdam（阿姆斯特丹大学计算机视觉研究组）
本文提出了一种混合实时摄像机姿态估计框架与一种新的分区方案，并引入运动平均单目同时定位与地图构建（SLAM）系统。突破了许多传统SLAM管道中固定大小的时间分区的限制，我们的方法通过将空间强连接的相机聚集到每个块中来显着提高局部光束法平差的精度。通过使用中间计算值的动态初始化，\XL{we improve the Levenberg-Marquardt solver to further enhance the efficiency of the local optimization.}此外，通过我们的基于共同可见性的划分，块之间的密集数据关联使我们能够探索和实现运动平均，以有效地全局对齐块，实时更新相机运动估计。基准上的实验令人信服地证明了我们提出的方法的实用性和鲁棒性显着优于传统的方法。
