SOTA

（指标 3D mAP， NDS，分割 mIOU）
可以查看nscenes 官网
https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Camera

2D 检测

Anchor-based方案
Two-stage Detectors
RCNN
Fast RCNN
Faster RCNN
One-stage Detectors
SSD
YOLO
Anchor-free方案
FCOS
CenterNet

Transformer方案：DETR

单目3d检测

先验几何信息
自动标注： 基于sam，点云投影到图像获取点云分割 label，生成3Dboxes

单目bev，一般是多目，小鹅通

3d bev cam范式

核心：视角转换
流派：
MLP： VPN，PON
LSS：BEVDET，BEVDET4D,bevdepth
Transformer： (DETR2d延伸)DETR3D, BEVFORMER, PETR, PETRV2

Transformer attention is all you need 2017

Transformer中selfatt和muitlhead-att

ViT vision transformer ICLR 2021google

TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE

非局部 network
https://blog.csdn.net/shanglianlm/article/details/104371212

DETR 2020

facebook
https://github.com/facebookresearch/detr
https://blog.csdn.net/weixin_43959709/article/details/115708159

BEIT： BERT Pre-Training of Image Transformer
https://blog.csdn.net/HX_Image/article/details/119177742

viT 2021
https://arxiv.org/pdf/2010.11929

DETR3D 2021

https://arxiv.org/pdf/2110.06922
https://github1s.com/WangYueFt/detr3d/tree/main

2D feat --> Decoder --> 3Dpred
ref-p query
https://github.com/WangYueFt/detr3d

transformer=dict(
type='Detr3DTransformer',
decoder=dict(type='Detr3DTransformerDecoder',num_layers=6,return_intermediate=True,transformerlayers=dict(type='DetrTransformerDecoderLayer',attn_cfgs=[dict(type='MultiheadAttention',embed_dims=256,num_heads=8,dropout=0.1),dict(type='Detr3DCrossAtten',pc_range=point_cloud_range,num_points=1,embed_dims=256)],feedforward_channels=512,ffn_dropout=0.1,operation_order=('self_attn', 'norm', 'cross_attn', 'norm','ffn', 'norm')))),
)

transformer 的层 一般6层，工业的话用3层，bevformer tiny 3层

PETR 2022

global attention 显存占用大
通过position embedding 利用 attention多视角图像特征关联

transformer=dict(type='PETRTransformer',decoder=dict(type='PETRTransformerDecoder',return_intermediate=True,num_layers=6,transformerlayers=dict(type='PETRTransformerDecoderLayer',attn_cfgs=[dict(type='MultiheadAttention',embed_dims=256,num_heads=8,dropout=0.1),dict(type='PETRMultiheadAttention',embed_dims=256,num_heads=8,dropout=0.1),],feedforward_channels=2048,ffn_dropout=0.1,with_cp=True,operation_order=('self_attn', 'norm', 'cross_attn', 'norm','ffn', 'norm')),)),

bevformer

比PETR的 全局注意力计算少，
（一般是多路聚合）
Deformable attention ——> 内外参bev空间索引 图像特征

git clone https://github.com/megvii-research/PETR.git

LSS

bevdet

LSS + centerPoint
IDA+BDA + scale NMS
input data augmentation, bev data augmentation

caddn

LSS + 深度监督
imvoxelNet

指标 mAP NDS

标注：基于点云（sam自动精度差），基于nerf （生成的数据集质量差一些）