Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then extract features by considering both of them. We also introduce a new dataset, named OpenLORIS-Location, to train the proposed network. The dataset contains 1553 images from 93 indoor locations. Various appearance changes between images of the same location are included and can help the model to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization. The RaP-Net code and dataset are available at https://github.com/ivipsourcecode/RaP-Net.
Plug to Place: Indoor Multimedia Geolocation from Electrical Sockets for Digital Investigation
Forensics and Security Research Group, School of Computer Science（犯罪学与安全研究组，计算机科学学院）；School of Engineering（工程学院）；Case Western Reserve University（凯斯西储大学）
Kalman Filtering for Precise Indoor Position and Orientation Estimation Using IMU and Acoustics on Riemannian Manifolds
Aramco Research Center（阿美石油研发中心）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Higher School of Communication of Tunis (Sup’Com)（突尼斯高等通信学院（Sup’Com））
AlignGS: Aligning Geometry and Semantics for Robust Indoor Reconstruction from Sparse Views
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China（信息科学与电子工程学院，上海交通大学，上海，中国）；Cooperative Mediant Innovation Center, Shanghai Jiao Tong University, Shanghai, China（协同医疗创新中心，上海交通大学，上海，中国）；SJTU Paris Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai, China（上海交通大学巴黎精英技术学院）
University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）
In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings. Given an input, potentially incomplete, 3D scene and a query location, our method predicts a probability distribution over object types that fit well in that location. Our distribution is predicted though passing learned messages in a dense graph whose nodes represent objects in the input scene and edges represent spatial and structural relationships. By weighting messages through an attention mechanism, our method learns to focus on the most relevant sur…
Point clouds have been widely adopted in 3D semantic scene understanding. However, point clouds for typical tasks such as 3D shape segmentation or indoor scenario parsing are much denser than outdoor LiDAR sweeps for the application of autonomous driving perception. Due to the spatial property disparity, many successful methods designed for dense point clouds behave depreciated effectiveness on the sparse data. In this paper, we focus on the semantic segmentation task of sparse outdoor point clouds. We propose a new method called MNEW, including multi-domain neighborhood embedding, and attenti…
Faster Bounding Box Annotation for Object Detection in Indoor Scenes
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Tampere University of Technology（坦佩雷理工大学）；Sandvik Mining and Construction Oyj（山特维克矿山与建筑公司）
A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening
University of Electronic Science and Technology of China（电子科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
This paper presents a method that can accurately detect heads especially small heads under the indoor scene. To achieve this, we propose a novel method, Feature Refine Net (FRN), and a cascaded multi-scale architecture. FRN exploits the multi-scale hierarchical features created by deep convolutional neural networks. The proposed channel weighting method enables FRN to make use of features alternatively and effectively. To improve the performance of small head detection, we propose a cascaded multi-scale architecture which has two detectors. One called global detector is responsible for detecti…
Shenzhen University（深圳大学）；Shenzhen TCL Industrial Research Institute Co., Ltd.（深圳TCL工业研究院有限公司）
Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques. They surpass traditional machine learning-based methods by a large margin in terms of accuracy and speed. Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions. In this survey, we firs…
Technical University of Munich（慕尼黑工业大学）；National University of Defense Technology（国防科技大学）；Siemens AG（西门子股份公司）
We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoints detected from dense points but usually struggle to guarantee their repeatability. To address this issue, we present CoFiNet - Coarse-to-Fine Network which extracts hierarchical correspondences from coarse to fine without keypoint detection. On a coarse scale and guided by a weighting scheme, our model firstly learns to match down-sampled nodes whose vicinity points share more overlap, which significantly shrinks …
