Search Results for author: Yiyang Zhou

Found 8 papers, 3 papers with code

Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects

no code implementations26 Sep 2022 Philip Jacobson, Yiyang Zhou, Wei Zhan, Masayoshi Tomizuka, Ming C. Wu

In this work, we propose a novel approach Center Feature Fusion (CFF), in which we leverage center-based detection networks in both the camera and LiDAR streams to identify relevant object locations.

3D Object Detection Autonomous Vehicles +1

What Matters for 3D Scene Flow Network

1 code implementation19 Jul 2022 Guangming Wang, Yunzhe Hu, Zhe Liu, Yiyang Zhou, Masayoshi Tomizuka, Wei Zhan, Hesheng Wang

Our proposed model surpasses all existing methods by at least 38. 2% on FlyingThings3D dataset and 24. 7% on KITTI Scene Flow dataset for EPE3D metric.

Scene Flow Estimation

SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera

no code implementations8 Jul 2022 Akio Kodaira, Yiyang Zhou, Pengwei Zang, Wei Zhan, Masayoshi Tomizuka

With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics.

Optical Flow Estimation Semantic Segmentation

DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection

1 code implementation17 Mar 2022 Jinhyung Park, Chenfeng Xu, Yiyang Zhou, Masayoshi Tomizuka, Wei Zhan

While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion.

object-detection Object Detection +2

A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding

1 code implementation6 Mar 2021 Di Feng, Yiyang Zhou, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan

Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving.

3D Object Detection Autonomous Driving +1

Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection

no code implementations18 Dec 2020 Di Feng, Zining Wang, Yiyang Zhou, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer, Masayoshi Tomizuka, Wei Zhan

As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.

Autonomous Driving object-detection +1

Inferring Spatial Uncertainty in Object Detection

no code implementations7 Mar 2020 Zining Wang, Di Feng, Yiyang Zhou, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer, Masayoshi Tomizuka, Wei Zhan

Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.

Autonomous Driving object-detection +1

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