Search Results for author: Junhao Cai

Found 5 papers, 2 papers with code

OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation

no code implementations19 Mar 2024 Junhao Cai, Yisheng He, Weihao Yuan, Siyu Zhu, Zilong Dong, Liefeng Bo, Qifeng Chen

Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation.

Object

Open-world Semantic Segmentation for LIDAR Point Clouds

1 code implementation4 Jul 2022 Jun Cen, Peng Yun, Shiwei Zhang, Junhao Cai, Di Luan, Michael Yu Wang, Ming Liu, Mingqian Tang

Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e. g., autonomous driving, since it is closed-set and static.

Autonomous Driving Incremental Learning +3

Open-set 3D Object Detection

no code implementations2 Dec 2021 Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu

The first step is solved by the finding that unknown objects are often classified as known objects with low confidence, and we show that the Euclidean distance sum based on metric learning is a better confidence score than the naive softmax probability to differentiate unknown objects from known objects.

3D Object Detection Clustering +3

Deep Metric Learning for Open World Semantic Segmentation

1 code implementation ICCV 2021 Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu

Incrementally learning these OOD objects with few annotations is an ideal way to enlarge the knowledge base of the deep learning models.

Autonomous Driving Few-Shot Learning +3

MetaGrasp: Data Efficient Grasping by Affordance Interpreter Network

no code implementations18 Feb 2019 Junhao Cai, Hui Cheng, Zhanpeng Zhang, Jingcheng Su

Although the model is trained using only RGB image, when changing the background textures, it also performs well and can achieve even 94% accuracy on the set of adversarial objects, which outperforms current state-of-the-art methods.

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