Search Results for author:
Found 27 papers, 12 papers with code
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment
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.
Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)
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.
A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation
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.
CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map
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.
A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation
The proposed algorithm is implemented with C++ and wrapped as a python function.
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding
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
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation
To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms.
Revisiting 2D Convolutional Neural Networks for Graph-based Applications
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation.
Enabling 3D Object Detection with a Low-Resolution LiDAR
Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization.
Deep Lucas-Kanade Homography for Multimodal Image Alignment
With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes.
A Surface Geometry Model for LiDAR Depth Completion
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.
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation
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.
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond
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.
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles
In this paper, a light-weight network is proposed for the task of LiDAR point cloud depth completion.
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
General graphs are difficult for learning due to their irregular structures.
RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation
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.
Automatic Building and Labeling of HD Maps with Deep Learning
This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicle.
A Survey on 3D LiDAR Localization for Autonomous Vehicles
LiDAR sensors are becoming one of the most essential sensors in achieving full autonomy for self driving cars.
PointNet on FPGA for Real-Time LiDAR Point Cloud Processing
LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization.
A Unified Hardware Architecture for Convolutions and Deconvolutions in CNN
In this paper, a scalable neural network hardware architecture for image segmentation is proposed.
Learning to Segment 3D Point Clouds in 2D Image Space
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.
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs
Graph convolutional networks (GCNs) suffer from the irregularity of graphs, while more widely-used convolutional neural networks (CNNs) benefit from regular grids.
A CNN Accelerator on FPGA Using Depthwise Separable Convolution
The state-of-the-art CNNs, such as MobileNetV2 and Xception, adopt depthwise separable convolution to replace the standard convolution for embedded platforms.
ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA
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.
Road Segmentation Using CNN and Distributed LSTM
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.
Road Segmentation Using CNN with GRU
This paper presents an accurate and fast algorithm for road segmentation using convolutional neural network (CNN) and gated recurrent units (GRU).
Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA
In this work, a convolutional neural network model is proposed and trained to perform semantic segmentation using the LiDAR sensor data.
Robotics
