no code implementations • 25 Apr 2024 • Zhiwei Dong, Xi Zhu, Xiya Cao, Ran Ding, Wei Li, Caifa Zhou, Yongliang Wang, Qiangbo Liu
B\'{e}zierFormer formulate queries as B\'{e}zier control points and incorporate a novel B\'{e}zier curve attention mechanism.
no code implementations • 17 Jun 2023 • Xi Zhu, Likang Wang, Caifa Zhou, Xiya Cao, Yue Gong, Lei Chen
The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment.
no code implementations • 7 Jun 2023 • Xi Zhu, Xiya Cao, Zhiwei Dong, Caifa Zhou, Qiangbo Liu, Wei Li, Yongliang Wang
We also provide a new scene-level BEV map evaluation setting along with the corresponding baseline for a more comprehensive comparison.
no code implementations • CVPR 2022 • Caifa Zhou, Xiya Cao, Dandan Zeng, Yongliang Wang
This paper introduces rotation-equivariance as a self-supervisor to train inertial odometry models.
2 code implementations • CVPR 2020 • Zan Gojcic, Caifa Zhou, Jan D. Wegner, Leonidas J. Guibas, Tolga Birdal
We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
no code implementations • 18 Dec 2019 • Caifa Zhou
One critical challenge is to controland improve the quality of the reference fingerprint map (RFM), which is built at the offline stage and applied for online positioning.
no code implementations • 20 May 2019 • Caifa Zhou, Andreas Wieser
In the positioning stage, the weights control the contribution of each feature to the dissimilarity measure, which in turn quantifies the difference between the set of online measured features and the fingerprints stored in the RFM.
2 code implementations • CVPR 2019 • Zan Gojcic, Caifa Zhou, Jan D. Wegner, Andreas Wieser
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
Ranked #4 on Point Cloud Registration on ETH (trained on 3DMatch)
no code implementations • 16 May 2018 • Caifa Zhou, Andreas Wieser
This proposed compound dissimilarity measure (CDM) is applicable to quantify similarity of collections of attribute/feature pairs where not all attributes are present in all collections.
no code implementations • 21 Nov 2017 • Caifa Zhou, Andreas Wieser
We propose an approach to reduce both computational complexity and data storage requirements for the online positioning stage of a fingerprinting-based indoor positioning system (FIPS) by introducing segmentation of the region of interest (RoI) into sub-regions, sub-region selection using a modified Jaccard index, and feature selection based on randomized least absolute shrinkage and selection operator (LASSO).
no code implementations • 2 Jun 2017 • Yang Gu, Caifa Zhou, Andreas Wieser, Zhimin Zhou
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users.
no code implementations • 17 May 2017 • Caifa Zhou, Yang Gu
This SVBI based position and RSS estimation has three properties: i) being able to predict the distribution of the estimated position and RSS, ii) treating each observation of RSS at each RP as an example to learn for FbP and RM generation instead of using the whole RM as an example, and iii) requiring only one time training of the SVBI model for both localization and RSS estimation.
no code implementations • 13 Apr 2017 • Lin Ma, Caifa Zhou, Xi Liu, Yubin Xu
By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results.
no code implementations • 12 Apr 2017 • Caifa Zhou, Lin Ma, Xuezhi Tan
Another significant innovation of this paper is jointing the fingerprint based algorithm with CM-SDE algorithm to improve the localization accuracy of indoor localization.
no code implementations • 14 Mar 2017 • Caifa Zhou, Andreas Wieser
We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage.