由于篇幅限制，论文总结（一）主要讲解基于激光雷达点云的3D检测方法(LiDAR only)。LiDAR only 指的是此类方法仅仅采用点云数据作为输入，方法的主要区分性在于对点云数据不同的特征提取方式。
1. Part-A^2 （TPAMI 2020）
链接：https://zhuanlan.zhihu.com/p/436797682
论文地址：
https://arxiv.org/pdf/1907.03670.pdf
作者单位：The Chinese University of Hong Kong
代码地址：
GitHub - open-mmlab/OpenPCDet: OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
一句话读论文：The ground-truth boxes of 3D object detection not only automatically provide accurate segmentation mask because of the fact that 3D objects are naturally separated in 3D scenes, but also imply the relative locations for each foreground 3D point within the ground truth boxes.
Part-aware stage
：作者认为前景点的相对位置(intra-object part location)可以表征物体的形状信息。因此，通过估计前景点的相对位置，作者认为可以得到更具有辨别性的特征。
The part-aware network aims to extract discriminative features from the point cloud by learning to estimate the intra-object part locations of foreground points, since these part locations implicitly encode the 3D object’s shapes by indicating the relative locations of surface points of 3D objects.
Part-aggregation stage
：既然是一个aggregation mechanism，作者具体聚合了哪些特征呢？文中作者主要融合了两部分特征，point-wise part location 以及 point-wise sementic features。利用融合后的特征，进一步预测每一个候选框的置信度和位置。
By considering the spatial distribution of the predicted intraobject part locations and the learned point-wise part features in a 3D box propsoal from stage-I, it is reasonable to aggregate all the information within a proposal for box proposal scoring and refinement.
2. Point RCNN （CVPR 2019）
链接：https://zhuanlan.zhihu.com/p/390767889
链接：https://zhuanlan.zhihu.com/p/436419513
论文地址：https://arxiv.org/pdf/1812.04244.pdf
作者单位：The Chinese University of Hong Kong
代码地址：https://github.com/sshaoshuai/PointRCN
N一句话读论文：The learned point representation from segmentation is not only good at proposal generation but is also helpful for the later box refinement.
网络框架
KITTI testset 实验结果
PointRCNN 整体为two-stage的框架，第一级生成proposal (Bottom-up 3D Proposal Generation)，第二级对proposal进行微调并得到最终的检测结果 (Canonical 3D Box Refinement)。
Bottom-up 3D Proposal Generation
：主要目的是做proposal的生成。对每一个point，提取point-wise feature，预测其属于前景点的概率和相应的proposal大小。对于生成的大量的proposal，利用NMS进行过滤，只保留其中的300个送入第二级进行微调。
We propose a novel bottom-up point cloud-based 3D bounding box proposal generation algorithm, which generates a small number of high-quality 3D proposals via segmenting the point cloud into foreground objects and background. The learned point representation from segmentation is not only good at proposal generation but is also helpful for the later box refinement.
Canonical 3D Box Refinement
：提取第一级proposal更精细的特征用于分类回归。更精细的特征包括：点特征+空间位置特征+RoI特征。
The proposed canonical 3D bounding box refinement takes advantages of our highrecall box proposals generated from stage-1 and learns to predict box coordinates refinements in the canonical coordinates with robust bin-based losses.
3. STD （ICCV 2019）
论文地址：
https://arxiv.org/pdf/1907.10471v1.pdf
作者单位：Youtu Lab, Tencent 等
一句话读论文：They propose a point-based proposal generation paradigm on point cloud with spherical anchors.
Considering that a 3D object could be with any orientations, we design spherical anchors rather than traditional cuboid anchors. As a result, the number of spherical anchors is not proportional to the number of pre-defined reference box orientation, leading to about 50% less anchors. With computation much reduced, we surprisingly achieve a much higher recall with spherical anchors than with traditional ones.
4. PV-RCNN/PV-RCNN++（CVPR 2020）
论文地址：
https://arxiv.org/pdf/2102.00463.pdf
作者单位：The Chinese University of Hong Kong
代码地址：
https://github.com/open-mmlab/OpenPCDet
一句话读论文：They propose a novel two-stage detection network for accurate 3D object detection through a two-step strategy of point-voxel feature aggregation.
Our proposed PV-RCNN-v1 first aggregates the voxel-wise scene features at multiple neural layers of 3D voxel CNN into a small number of keypoints, which bridge the 3D voxel CNN feature encoder and the proposal refinement network.
In this step, we propose keypoint-to-grid RoI feature abstraction to generate accurate proposal-aligned features from the keypoint features for fine-grained proposal refinement.
6. PointPillar（CVPR 2019）
论文地址：
https://arxiv.org/pdf/1812.05784.pdf
作者单位：Oscar Beijbom and nuTonomy: an APTIV company
一句话读论文：A novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars).
The points in each pillar are then augmented with xc, yc, zc, xp and yp where the c subscript denotes distance to the arithmetic mean of all points in the pillar and the p subscript denotes the offset from the pillar x; y center.
2. D×P×N → C×P×N 通过
卷积运算
实现。
Next, we use a simplified version of PointNet where, for each point, a linear layer is applied followed by Batch- Norm and ReLU to generate a C×P×N sized tensor.
3. C×P×N → C×P是
对N维度做max运算
，类似
max pooling
。
This is followed by a max operation over the channels to create an output tensor of size C×P.
Once encoded, the features are scattered back to the original pillar locations to create a
pseudo-image
of size C×H×W where H and W indicate the height and width of the canvas.
至此，其实核心部分已经处理完毕，接下来可以用我们熟知的检测方法处理此点云数据。
7. MVP（NIPS 2021）
论文地址：
https://arxiv.org/pdf/2111.06881.pdf
作者单位：
https://www.utexas.edu/
代码地址：
https://tianweiy.github.io/mvp/
一句话读论文：The approach takes a set of 2D detections to generate dense 3D virtual points to augment an otherwise sparse 3D point cloud.
网络框架
NuScenes testset 实验结果
这篇文章的核心内容是
如何利用2D信息对3D点云进行补全
，
也就是2D instance segmentation → virtual 3D points
。作者的具体做法是：3D raw points → 2D points → 3D virtual points。
3D raw points → 2D points。这一步是将3D物体上的所有点映射到2D图像上，但是映射之后的点仅考虑落在对应物体segmentation区域内的。
We start by projecting the 3D Lidar point cloud onto our detection. Specifically, we transform each Lidar point into the reference frame of the RGB camera, then project it into image coordinates with associated depth using a perspective projection. The frustum only considers projected 3D points that fall within a detection mask. Any Lidar measurement outside detection masks is discarded.
2. 2D points → 3D virtual points。我们知道，3D到2D的映射属于降维，而2D到3D的映射属于升维。因此，如果没有增加额外的depth信息，从2D映射到3D有无穷多解，这显然是不合适的。作者尝试通过补全每一个2D point的depth信息，将原本“一到无穷”的映射变换为“一到一”的映射。更具体的，对于一个random 2D point，作者将其depth估计为其最邻近的映射3D raw point的depth。
We start by randomly sampling 2D points from each instance mask. We sample points uniformly at random without repetition. For each sampled point, we retrieve a depth estimate from its nearest neighbor in the frustum.
8. SE-SSD（CVPR 2021）
论文地址：
arxiv.org/pdf/2104.0980
作者单位：The Chinese University of Hong Kong
代码地址：
GitHub - Vegeta2020/SE-SSD: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud, CVPR 2021.
一句话读论文：The key focus is on exploiting both soft and hard targets with formulated constraints to jointly optimize the model, without introducing extra computation in the inference.
https://zhuanlan.zhihu.com/p/371520457
https://zhuanlan.zhihu.com/p/390167950
网络框架
KITTI testset 实验结果
这篇文章的核心其实更偏向于augmentation，从本质上讲，本文中包含的两个核心模块
soft targets生成训练（label space augmentation）
和
Shape-aware data augmentation (target space augmentation)
均属于augmentation范畴。通过ensemble的形式，可以同时利用soft targets和hard targets的信息。
a）Hence, we exploit both soft and hard targets with our formulated constraints to jointly optimize the model, while incurring no extra inference time.
b）On the other hand, to enable the student SSD to effectively explore a larger data space, we design a new augmentation scheme on top of conventional augmentation strategies to produce augmented object samples in a shape-aware manner.
更具体地，有两个注意点：
第一，
Shape-aware Data Augmentation
是怎么做的？首先，shape-aware尝试解决的问题是：same samples but with different point representation，用作者文中的话说就是由于遮挡距离等问题的存在，相同物体具有不同的点云表达。
Our insight comes from the observation that the point cloud patterns of ground-truth objects could vary significantly due to occlusions, changes in distance, and diversity of object shapes in practice.
Our consistency loss is to align the student predictions with the soft targets and when we augment the input, we bring along its hard targets to supervise the student with our orientationaware distance-IoU loss.
9. SA-SSD（CVPR 2020）
论文地址：
https://www4.comp.polyu.edu.hk/~cslzhang/paper/SA-SSD.pdf
作者单位：The Hong Kong Polytechnic University 等
代码地址：
https://github.com/skyhehe123/SA-SSD
一句话读论文：The auxiliary network is introduced, which is jointly optimized by two point-level supervisions, to guide the convolutional features in the backbone network to be aware of the object structure.
We propose a structure-aware single-stage 3D object detector, which employs a detachable auxiliary network to learn structure information and exhibits better localization performance without extra cost.
Specifically, we employ a sigmoid function to the segmentation branch to predict the foreground/background probability of each point. The segmentation task enables the backbone network to more precisely detect the object boundary.
To further improve the localization accuracy, we employ another auxiliary task to learn the relative position of each object point to the object center. This intra-object relationship can help determine the scale and shape of the object, resulting in more precise localization.
