3D Object Detection Using Scale Invariant and Feature Reweighting Networks
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 network architecture which focuses on utilizing the front view images and frustum point clouds to generate 3D detection results. On the one hand, a PointSIFT module is utilized to improve the performance of 3D segmentation. It can capture the information from different orientations in space and the robustness to different scale shapes. On the other hand, our network obtains the useful features and suppresses the features with less information by a SENet module. This module reweights channel features and estimates the 3D bounding boxes more effectively. Our method is evaluated on both KITTI dataset for outdoor scenes and SUN-RGBD dataset for indoor scenes. The experimental results illustrate that our method achieves better performance than the state-of-the-art methods especially when point clouds are highly sparse.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object Detectionobject-detectionObject DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Source-Free Object Detection with Detection Transformer
Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data. Most existing SFOD approaches are either confined t…
Contrastive LearningObject DetectionMitigating Intensity Bias in Shadow Detection via Feature Decomposition and Reweighting
While CNNs achieved remarkable progress in shadow detection, they tend to make mistakes in dark non-shadow regions and relatively bright shadow regions. They are also susceptible to brightness change. These two pheno…
Shadow DetectionI3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors
Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the region proposal mechanisms and ROI-based ins…
Region ProposalBoost UAV-based Ojbect Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning
Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream approaches typically utilize multi-stage …
Disentanglementobject-detectionObject DetectionFew-shot Object Detection via Feature Reweighting
Conventional training of a deep CNN based object detector demands a large number of bounding box annotations, which may be unavailable for rare categories. In this work we develop a few-shot object detector that can lear…
Few-Shot LearningFew-Shot Object DetectionImage ClassificationMeta-Learning+3