ithink3iam / TiG-BEV

Target Inner-Geometry Learning for BEV 3D Object Detection

TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning

Official implementation of 'TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning'.

Introduction

We propose TiG-BEV, a learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features. First, we introduce an inner-depth supervision module to learn the low-level relative depth relations between different foreground pixels. This enables the camerabased detector to better understand the object-wise spatial structures. Second, we design an inner-feature BEV distillation module to imitate the high-level semantics of different keypoints within foreground targets. To further alleviate the BEV feature gap between two modalities, we adopt both inter-channel and inter-keypoint distillation for feature-similarity modeling. With our target inner-geometry distillation, TiG-BEV can effectively boost BEVDepth by +2.3% NDS and +2.4% mAP, along with BEVDet by +9.1% NDS and +10.3% mAP on nuScenes val set.

Main Results

Method	mAP	NDS
TiG-BEV-R50	33.8	37.5
TiG-BEV4D-R50	36.6	46.1

We provide the model and log of TiG-BEV4D-R101-CBGS.

Method	mAP	NDS	Model	Log
TiG-BEV4D-R101-CBGS	44.0	54.4	Google	Google

Get Started

Installation and Data Preparation

Please see getting_started.md in BEVDet.

Acknowledgement

We sincerely thank these great open-sourced work below:

• open-mmlab
• CenterPoint
• Lift-Splat-Shoot
• BEVDet
• BEVDepth
• BEVFusion

Bibtex

If you find this project useful, please cite:

@article{huang2022tig,
  title={TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning},
  author={Huang, Peixiang and Liu, Li and Zhang, Renrui and Zhang, Song and Xu, Xinli and Wang, Baichao and Liu, Guoyi},
  journal={arXiv preprint arXiv:2212.13979},
  year={2022}
}