[BEV] 学习笔记之Lift, Splat, Shoot

在开源的BEV模型中,可以追溯到2020年nvidia开源的 Lift-Splat- Shoot这篇论文中，论文的核心是显示的估计图像的深度特征，并转化为BEV特征，作为BEV视角下的开山鼻祖，自然是要对原理以及代码进行阅读，本文将对关键代码进行注释，欢迎正在学习或者想学习BEV模型的朋友加入交流群一起讨论、学习论文或者代码实现中的问题 ，v:Rex1586662742，数字为q。

项目地址:https://github.com/nv-tlabs/lift-splat-shoot
学习视频:手撕BEV的开山之作 lift, splat, shoot

2、模型简介

LSS这篇论文主要的贡献在于提出LIFT，在视锥的条件下，显示的预测了每个特征点的离散距离，如下图所示。

3、前向推理

通过对推理的梳理，可以理解论文的思路，以及在代码中的实现，下面为foward的过程。
1、src/explore.py

def viz_model_preds(...):
    '''
    xbound=[-50.0, 50.0, 0.5], bev特征大小
    ybound=[-50.0, 50.0, 0.5],
    zbound=[-10.0, 10.0, 20.0], LSS论文中不计算物体的高度
    dbound=[4.0, 45.0, 1.0], 离散深度尺寸，间隔为1
    '''
    out = model(...)
    # 进入到/data/cv_demo/bev/lift-splat-shoot/src/models.py -> forward(...)

2、src/models.py

class LiftSplatShoot(...):
    def __init__(...):、
        # 生成视锥
        self.frustum = self.create_frustum()
        # 图像特征提取
        self.camencode = CamEncode(self.D, self.camC, self.downsample)
        # bev特征提取
        self.bevencode = BevEncode(inC=self.camC, outC=outC)
        
    def create_frustum(...):
        # 原图高宽
        ogfH, ogfW = self.data_aug_conf['final_dim']
        # 特征图宽高
        fH, fW = ogfH // self.downsample, ogfW // self.downsample\
        # 距离 ds:[[41, 8, 22]]  每个特征点预测41个距离
        ds = ...
        # 视锥x坐标 xs:[41, 8, 22]
        xs  = ...
        # 视锥y坐标  xs:[41, 8, 22]
        ys = ...
        # 视锥点云 frustum:[41, 8, 22, 3]      
        frustum = torch.stack((xs, ys, ds), -1)
        
    def get_geometry(...):
        """
        param:rots, trans, intrins, post_rots, post_trans
        """
        # 视锥点云减去图像预处理的平移矩阵
        points = self.frustum - post_trans.view(B, N, 1, 1, 1, 3)
        # 视锥点云乘以图像与处理的旋转矩阵的逆矩阵
        points = torch.inverse(post_rots).view(B, N, 1, 1, 1, 3, 3).matmul(points.unsqueeze(-1))
        # 图像坐标系转相机坐标系,对应转化的公式
        # xs,ys,lamda
        # lamda * xs, lamda *ys, lamda 
        points = torch.cat((points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3],
                            points[:, :, :, :, :, 2:3]
                            ), 5)
        # 相机坐标系转ego坐标系
        combine = rots.matmul(torch.inverse(intrins))
        points = combine.view(B, N, 1, 1, 1, 3, 3).matmul(points).squeeze(-1)
        points += trans.view(B, N, 1, 1, 1, 3)
        return points
        
    def get_cam_feats(...):
        """
        x:环视图片 [4, 6, 3, 128, 352]
        """
        # 图片深度特征提取 -> [24, 64, 41, 8, 22] 
        x = self.camencode(x)
        x = x.view(B, N, self.camC, self.D, imH//self.downsample, imW//self.downsample)
        # [4, 6, 41, 8, 22, 64]
        x = x.permute(0, 1, 3, 4, 5, 2)
        return x

    def voxel_pooling(...):
        """
        geom_feats:ego坐标系下的视锥点云 [4, 6, 41, 8, 22, 3]
        x:环视图像的特征  [4, 6, 41, 8, 22, 64]
        """
        Nprime = B*N*D*H*W # 视锥点的个数
        # x:[173184, 64]
        x = x.reshape(Nprime, C) 
        # 想坐标系的中心移动到左下角bev空间的(原本是bev空间的中心)
        geom_feats = ((geom_feats - (self.bx - self.dx/2.)) / self.dx).long()
        # 所有视锥点的坐标 geom_feats:[173184, 3]
        geom_feats = geom_feats.view(Nprime, 3)
        # batch_ix:[173184, 1] [0,0,0,...,1,1,1,...,2,2,2] 记录每个点在那个batch内
        batch_ix = ...
        
        # 过滤不在bev空间下的点
        kept = ...
        x = x[kept]
        geom_feats = geom_feats[kept]
        # x,y,z,b  将多个相机中重合点排到一起
        # (x*200 + y * 1 + z * 1 + 1) * B
        ranks = ...
        sorts = ranks.argsort()
        # 将xyz相同的点排到一起
        x, geom_feats, ranks = x[sorts], geom_feats[sorts], ranks[sorts]
        # rank中一样的值，只取最后一个
        # 例如rank为 [1,1,2,2,2,3,3,3,3,3]
        # 将其转化为 [0,1,0,0,1,0,0,0,0,1]然后将为1的点取出来
        if not self.use_quickcumsum:
            x, geom_feats = cumsum_trick(x, geom_feats, ranks)
        else:
            ...
        # final:[4, 64, 1, 200, 200]  bev 特征
        final = ...
        return final
        
    def get_voxels(...):
        geom = self.get_geometry(rots, trans, intrins, post_rots, post_trans)
        x = self.get_cam_feats(x)
        # x:[4, 64, 200, 200] bev特征
        x = self.voxel_pooling(geom, x)
        return x
        
    def forward(...):
        """
        x:imgs,环视图片  (bs, N, 3, H, W)
        rots:由相机坐标系->车身坐标系的旋转矩阵，rots = (bs, N, 3, 3)
        trans:由相机坐标系->车身坐标系的平移矩阵，trans=(bs, N, 3)
        intrins:相机内参，intrinsic = (bs, N, 3, 3)
        post_rots:由图像增强引起的旋转矩阵，post_rots = (bs, N, 3, 3)
        post_trans:由图像增强引起的平移矩阵，post_trans = (bs, N, 3)
        """
        x = self.get_voxels(x, rots, trans, intrins, post_rots, post_trans)
        # 卷积
        x = self.bevencode(x)
        return x 
# LIFT
class CamEncode(...):
    def __init__(...):
        self.D = D # 离散距离  41
        self.C = C # 特征维度  64
        # 图片特征提取
        self.trunk = ... 
        # 上采样模块
        self.up1 = ...
        # 深度特征提取网络
        self.depthnet = ...
    
    def get_depth_feat(...):
        # 使用efficientnet提取特征 x: 24 x 512 x 8 x 22
        x = self.get_eff_depth(x) 
        # 预测深度特征
        x = self.depthnet(x)
        # 深度
        depth = self.get_depth_dist(x[:, :self.D])
        # 深度特征
        new_x = ...
        return depth, new_x
# Splat
class BevEncode(...):
    def __init__(...):
        ...
    def forward(...)
        ...

3、src/tools.py

class SimpleLoss(torch.nn.Module):
    def __init__(self, pos_weight):
        super(SimpleLoss, self).__init__()
        # sigmoid+二值交叉熵损失
        self.loss_fn = torch.nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([pos_weight]))

    def forward(self, ypred, ytgt):
        loss = self.loss_fn(ypred, ytgt)
        return loss

4、总结

以上为LSS算法实现的基本流程，如有错误，欢迎大家进行指正，后续还要学习其他BEV系列的模型，希望能对BEV模型有进一步的理解。