paper-with-me

홈 › Papers

PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

2018-07-02 · Mingyang Jiang, Yiran Wu, Tianqi Zhao, Zelin Zhao, Cewu Lu

Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds. Inspired by the outstanding 2D shape descriptor SIFT, we design a module called PointSIFT that encodes information of different orientations and is adaptive to scale of shape. Specifically, an orientation-encoding unit is designed to describe eight crucial orientations, and multi-scale representation is achieved by stacking several orientation-encoding units. PointSIFT module can be integrated into various PointNet-based architecture to improve the representation ability. Extensive experiments show our PointSIFT-based framework outperforms state-of-the-art method on standard benchmark datasets. The code and trained model will be published accompanied by this paper.

📄 PDF Abstract BibTeX arXiv:1807.00652

Code (4)

583748495/psift tf
LyapunovShao/pointSIFT tf
MVIG-SJTU/pointSIFT tf
Xiangxu-0103/Octant-CNN tf

Tasks

Point Cloud SegmentationSemantic Segmentation

Similar Papers 제목 키워드 기반

3D Object Detection Using Scale Invariant and Feature Reweighting Networks

2019-01-08 · Xin Zhao, Zhe Liu, Ruolan Hu, Kaiqi Huang

3D object detection plays an important role in a large number of real-world applications. It requires us to estimate the localizations and the orientations of 3D objects in real scenes. In this paper, we present a new ne…

3D Object Detectionobject-detectionObject Detection

Performance Evalution of 3D Keypoint Detectors and Descriptors for Plants Health Classification

2019-04-02 · Shiva Azimi, Brejesh lall, Tapan K. Gandhi

Plant Phenomics based on imaging based techniques can be used to monitor the health and the diseases of plants and crops. The use of 3D data for plant phenomics is a recent phenomenon. However, since 3D point cloud conta…

General Classification

Revealing Scenes by Inverting Structure from Motion Reconstructions

2019-04-05 · CVPR 2019 6 · Francesco Pittaluga, Sanjeev J. Koppal, Sing Bing Kang, Sudipta N. Sinha

Many 3D vision systems localize cameras within a scene using 3D point clouds. Such point clouds are often obtained using structure from motion (SfM), after which the images are discarded to preserve privacy. In this pape…

PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector

2022-06-17 · Remote Sensing 2022 6 · Mingfei Gu, Yinghua Wang *, Hongwei Liu and Penghui Wang

The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extra…

See SIFT in a Rain

2023-11-01 · Wei Wu, Hao Chang, Zhu Li

Rain streaks bring complicated pixel intensity changes and additional gradients, greatly obstructing the extraction of image features from background. This causes serious performance degradation in feature-based applicat…

Rain Removal