no code implementations • CVPR 2023 • Yiqing Zhang, Xinming Huang, Ziming Zhang
The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs have large distortions.
1 code implementation • 5 Feb 2023 • Ming Li, Xinming Huang, Ziming Zhang
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors.
no code implementations • 12 Jul 2022 • Lin Bai, Yiming Zhao, Xinming Huang
In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network.
no code implementations • 19 Oct 2021 • Yecheng Lyu, Xinming Huang, Ziming Zhang
In addition, we propose a map based LiDAR localization algorithm that extracts semantic feature points from the LiDAR frames and apply CoFi to estimate the pose on an efficient point cloud map.
1 code implementation • 16 Sep 2021 • Yiming Zhao, Xiao Zhang, Xinming Huang
The proposed algorithm is implemented with C++ and wrapped as a python function.
1 code implementation • 8 Sep 2021 • Yiming Zhao, Lin Bai, Xinming Huang
In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step.
LIDAR Semantic Segmentation Robust 3D Semantic Segmentation +1
1 code implementation • 21 Aug 2021 • Yiming Zhao, Xiao Zhang, Xinming Huang
To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms.
no code implementations • 23 May 2021 • Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation.
no code implementations • 4 May 2021 • Lin Bai, Yiming Zhao, Xinming Huang
Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization.
1 code implementation • CVPR 2021 • Yiming Zhao, Xinming Huang, Ziming Zhang
With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes.
1 code implementation • 17 Apr 2021 • Yiming Zhao, Lin Bai, Ziming Zhang, Xinming Huang
Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error.
no code implementations • 3 Mar 2021 • Yecheng Lyu, Xinming Huang, Ziming Zhang
In recent years, point cloud representation in 2D space has attracted increasing research interest since it exposes the local geometry features in a 2D space.
1 code implementation • ICCV 2021 • Ming Li, Xinming Huang, Ziming Zhang
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors.
1 code implementation • 5 Jul 2020 • Lin Bai, Yiming Zhao, Mahdi Elhousni, Xinming Huang
In this paper, a light-weight network is proposed for the task of LiDAR point cloud depth completion.
1 code implementation • 21 Jun 2020 • Yecheng Lyu, Ming Li, Xinming Huang, Ulkuhan Guler, Patrick Schaumont, Ziming Zhang
General graphs are difficult for learning due to their irregular structures.
1 code implementation • 13 Jun 2020 • Lin Bai, Yecheng Lyu, Xinming Huang
In order to reach real-time process speed, a light-weight, high-throughput CNN architecture namely RoadNet-RT is proposed for road segmentation in this paper.
no code implementations • 1 Jun 2020 • Mahdi Elhousni, Yecheng Lyu, Ziming Zhang, Xinming Huang
This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicle.
no code implementations • 1 Jun 2020 • Mahdi Elhousni, Xinming Huang
LiDAR sensors are becoming one of the most essential sensors in achieving full autonomy for self driving cars.
no code implementations • 29 May 2020 • Lin Bai, Yecheng Lyu, Xinming Huang
In this paper, a scalable neural network hardware architecture for image segmentation is proposed.
no code implementations • 29 May 2020 • Lin Bai, Yecheng Lyu, Xin Xu, Xinming Huang
LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization.
1 code implementation • CVPR 2020 • Yecheng Lyu, Xinming Huang, Ziming Zhang
In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation.
no code implementations • 26 Sep 2019 • Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) suffer from the irregularity of graphs, while more widely-used convolutional neural networks (CNNs) benefit from regular grids.
no code implementations • 3 Sep 2018 • Lin Bai, Yiming Zhao, Xinming Huang
The state-of-the-art CNNs, such as MobileNetV2 and Xception, adopt depthwise separable convolution to replace the standard convolution for embedded platforms.
2 code implementations • 10 Aug 2018 • Yecheng Lyu, Lin Bai, Xinming Huang
In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning.
no code implementations • 10 Aug 2018 • Yecheng Lyu, Lin Bai, Xinming Huang
This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time.
no code implementations • 14 Apr 2018 • Yecheng Lyu, Xinming Huang
This paper presents an accurate and fast algorithm for road segmentation using convolutional neural network (CNN) and gated recurrent units (GRU).
no code implementations • 7 Nov 2017 • Yecheng Lyu, Lin Bai, Xinming Huang
In this work, a convolutional neural network model is proposed and trained to perform semantic segmentation using the LiDAR sensor data.
Robotics