Search Results for author:
Found 17 papers, 11 papers with code
Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition
Our proposed method can achieve accurate map matching even in complex road networks by carefully leveraging lane markings and scenario recognition in the designing of the probability factors.
Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous Driving
Recent studies have highlighted the promising application of NeRF in autonomous driving contexts.
Deep Planar Parallax for Monocular Depth Estimation
Recent research has highlighted the utility of Planar Parallax Geometry in monocular depth estimation.
Prediction of GPU Failures Under Deep Learning Workloads
To improve the precision and stability of predictions, we propose several techniques, including parallel and cascade model-ensemble mechanisms and a sliding training method.
SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking
3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm.
Ranked #34 on
3D Multi-Object Tracking
on nuScenes
Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem
With MIG, A100 can be the most cost-efficient GPU ever for serving Deep Neural Networks (DNNs).
Comparison between safety methods control barrier function vs. reachability analysis
This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis.
Unsupervised Scale-consistent Depth Learning from Video
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.
LiDAR R-CNN: An Efficient and Universal 3D Object Detector
LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving.
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
The code and protocols for our benchmark and algorithm are available at https://github. com/TuSimple/LiDAR_SOT/.
Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing.
Systems and Control Robotics Systems and Control
DMLO: Deep Matching LiDAR Odometry
LiDAR odometry is a fundamental task for various areas such as robotics, autonomous driving.
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.
Ranked #4 on
Camera Pose Estimation
on KITTI Odometry Benchmark
VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing
In this paper, we propose a novel network design mechanism for efficient embedded computing.
Ranked #5 on
Face Verification
on CFP-FP
BigDL: A Distributed Deep Learning Framework for Big Data
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.
Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place.
Dynamic Computational Time for Visual Attention
We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM).
