Paper Interpretation | Center-based 3D Object Detection and Tracking - Code World

Paper Interpretation | Center-based 3D Object Detection and Tracking

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Origin blog.csdn.net/Hinyeung2021/article/details/132762260

Paper Interpretation | Center-based 3D Object Detection and Tracking

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

Interpretation of the paper｜VoxelNet: End-to-end learning for point cloud-based 3D object detection

MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection Paper Interpretation

Paper interpretation | Original PV-RCNN network for 3D object detection

[Interpretation of the paper] Camouflaged Object Detection

Interpretation of the paper: High-quality object tracking

[Paper Reading] Multi-Object Tracking—ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box

Paper Interpretation--CNN based Road User Detection using the 3D Radar Cube

Interpretation of the paper--Robust lane detection and tracking with Ransac and Kalman filter

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

1、CenterPoint: Center-based 3D Object Detection and Tracking 环境配置

Paper Interpretation--K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

Interpretation of the paper: End-to-End Object Detection with Transformers

Paper's EfficientDet: Translation and Interpretation of "Scalable and Efficient Object Detection—Scalable and Efficient Object Detection"—Sequel

[Target detection] Target detection meets knowledge graph: Interpretation and reproduction of Object detection meets knowledge graphs paper

Paper reading-Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Image

3D box classic paper - "Multimodal 3D Object Detection from Simulated Pretraining" learning record

Paper reading: "Bridging the Gap in 3D Object Detection for Autonomous Driving"

Paper reading: EPNet: Enhancing Point Features with ImageSemantics for 3D Object Detection

Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection - Paper Notes

[Paper Notes] MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection

[Paper Notes] SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection

[Paper Notes] Cross Modal Transformer: Towards Fast and Robust 3D Object Detection

[Paper Notes] PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds

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