Search Results for author: Shaoshuai Shi

Found 15 papers, 10 papers with code

Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation

no code implementations ICCV 2021 Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, Chi-Wing Fu, Jiaya Jia

To address the high cost and challenges of 3D point-level labeling, we present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance.

3D Semantic Segmentation Contrastive Learning

LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector

no code implementations ICCV 2021 Xiaoyang Guo, Shaoshuai Shi, Xiaogang Wang, Hongsheng Li

Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10. 44%, 5. 69%, 5. 97% mAP respectively on the official KITTI benchmark.

Stereo Matching

ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection

no code implementations15 Aug 2021 Jihan Yang, Shaoshuai Shi, Zhe Wang, Hongsheng Li, Xiaojuan Qi

These specific designs enable the detector to be trained on meticulously refined pseudo labeled target data with denoised training signals, and thus effectively facilitate adapting an object detector to a target domain without requiring annotations.

3D Object Detection Data Augmentation +2

Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

1 code implementation CVPR 2021 Bowen Cheng, Lu Sheng, Shaoshuai Shi, Ming Yang, Dong Xu

Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds.

3D Object Detection

ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

1 code implementation CVPR 2021 Jihan Yang, Shaoshuai Shi, Zhe Wang, Hongsheng Li, Xiaojuan Qi

Then, the detector is iteratively improved on the target domain by alternatively conducting two steps, which are the pseudo label updating with the developed quality-aware triplet memory bank and the model training with curriculum data augmentation.

3D Object Detection Data Augmentation +1

Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

4 code implementations31 Dec 2020 Jiajun Deng, Shaoshuai Shi, Peiwei Li, Wengang Zhou, Yanyong Zhang, Houqiang Li

In this paper, we take a slightly different viewpoint -- we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer sufficient detection accuracy.

3D Object Detection Region Proposal

PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges

1 code implementation28 Aug 2020 Shaoshuai Shi, Chaoxu Guo, Jihan Yang, Hongsheng Li

In this technical report, we present the top-performing LiDAR-only solutions for 3D detection, 3D tracking and domain adaptation three tracks in Waymo Open Dataset Challenges 2020.

3D Object Detection Domain Adaptation

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

5 code implementations CVPR 2020 Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li

We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.

3D Object Detection

From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

4 code implementations8 Jul 2019 Shaoshuai Shi, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li

3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.

3D Object Detection Scene Understanding

Feature Intertwiner for Object Detection

2 code implementations ICLR 2019 Hongyang Li, Bo Dai, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang

We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher behavior and thus pushing towards a more compact class centroid in the feature space.

Object Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.