1 code implementation • 9 Mar 2024 • Junyi Cao, Zhichao Li, Naiyan Wang, Chao Ma
Recent studies have highlighted the promising application of NeRF in autonomous driving contexts.
no code implementations • 9 Jan 2023 • Haoqian Liang, Zhichao Li, Ya Yang, Naiyan Wang
Recent research has highlighted the utility of Planar Parallax Geometry in monocular depth estimation.
no code implementations • 27 Jan 2022 • Heting Liu, Zhichao Li, Cheng Tan, Rongqiu Yang, Guohong Cao, Zherui Liu, Chuanxiong Guo
To improve the precision and stability of predictions, we propose several techniques, including parallel and cascade model-ensemble mechanisms and a sliding training method.
2 code implementations • 18 Nov 2021 • Ziqi Pang, Zhichao Li, Naiyan Wang
3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm.
Ranked #2 on 3D Multi-Object Tracking on Waymo Open Dataset
no code implementations • 18 Sep 2021 • Cheng Tan, Zhichao Li, Jian Zhang, Yu Cao, Sikai Qi, Zherui Liu, Yibo Zhu, Chuanxiong Guo
With MIG, A100 can be the most cost-efficient GPU ever for serving Deep Neural Networks (DNNs).
no code implementations • 24 Jun 2021 • Zhichao Li
This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis.
2 code implementations • 25 May 2021 • Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.
1 code implementation • CVPR 2021 • Zhichao Li, Feng Wang, Naiyan Wang
LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving.
4 code implementations • 10 Mar 2021 • Ziqi Pang, Zhichao Li, Naiyan Wang
The code and protocols for our benchmark and algorithm are available at https://github. com/TuSimple/LiDAR_SOT/.
no code implementations • 14 May 2020 • Zhichao Li, Thai Duong, Nikolay Atanasov
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing.
Systems and Control Robotics Systems and Control
no code implementations • 8 Apr 2020 • Zhichao Li, Naiyan Wang
LiDAR odometry is a fundamental task for various areas such as robotics, autonomous driving.
2 code implementations • NeurIPS 2019 • Jia-Wang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid
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 #61 on Monocular Depth Estimation on KITTI Eigen split
1 code implementation • 12 Jul 2019 • Qian Zhang, Jianjun Li, Meng Yao, Liangchen Song, Helong Zhou, Zhichao Li, Wenming Meng, Xuezhi Zhang, Guoli Wang
In this paper, we propose a novel network design mechanism for efficient embedded computing.
Ranked #5 on Face Verification on CFP-FP
1 code implementation • 16 Apr 2018 • Jason Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Zhang, Yan Wan, Zhichao Li, Jiao Wang, Shengsheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song
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.
1 code implementation • 14 Jul 2017 • Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie zhou, Shilei Wen
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.
1 code implementation • 30 Mar 2017 • Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, Wei Xu
We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM).