三维重建

资料汇总：https://github.com/openMVG/awesome_3DReconstruction_list

单目图像

主要分为基于SfM三维重建和基于Deep learning的三维重建方法，sfM方法在下节将会做详细介绍，基于深度学习方式，主要通过RGB图像生成深度图。

Paper

1. Unsupervised Monocular Depth Estimation with Left-Right Consistency
2. Unsupervised Learning of Depth and Ego-Motion from Video
3. Deep Ordinal Regression Network for Monocular Depth Estimation
4. Depth from Videos in the Wild
5. Attention-based Context Aggregation Network for Monocular Depth Estimation
6. Depth Map Prediction from a Single Image using a Multi-Scale Deep Network（NIPS2014）
7. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture（ICCV2015)
8. Deeper Depth Prediction with Fully Convolutional Residual Networks
9. Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation(CVPR2017)
10. Single View Stereo Matching

Project with code

Project	Paper	Framework
3dr2n2: A unified approach for single and multi-view 3d object Reconstruction	ECCV 2016	Theano
Learning a predictable and generative vector representation for objects	ECCV 2016	Caffe
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling	NIPS 2016	Torch 7
Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision	NIPS 2016	Torch 7
Deep disentangled representations for volumetric reconstruction	ECCV 2016
Multi-view 3D Models from Single Images with a Convolutional Network	ECCV 2016	Tensorflow
Single Image 3D Interpreter Network	ECCV 2016	Torch 7
Weakly-Supervised Generative Adversarial Networks for 3D Reconstruction	3DV 2017	Theano
Hierarchical Surface Prediction for 3D Object Reconstruction	3DV 2017	Torch 7
Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs	ICCV 2017	Caffe
Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency	CVPR 2017	Torch 7
SurfNet: Generating 3D shape surfaces using deep residual networks	CVPR 2017	Matlab
A Point Set Generation Network for 3D Object Reconstruction from a Single Image	CVPR 2017	Tensorflow
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis	SIGGRAPH 2017	Caffe
Rethinking Reprojection: Closing the Loop for Pose-aware Shape Reconstruction from a Single Image	ICCV 2017
Scaling CNNs for High Resolution Volumetric Reconstruction From a Single Image	ICCV 2017
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55	ICCV 2017
Learning a Hierarchical Latent-Variable Model of 3D Shapes	3DV 2018	Tensorflow
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction	AAAI 2018	Tensorflow
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image	ACCV 2018	Tensorflow
Image2Mesh: A Learning Framework for Single Image 3DReconstruction	ACCV 2018	Pytorch
Neural 3D Mesh Renderer	CVPR 2018	Chainer
Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction	CVPR 2018	Torch 7
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers	CVPR 2018	Pytorch
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation	CVPR 2018	Pytorch
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images	ECCV 2018	Tensorflow
Multiresolution Tree Networks for 3D Point Cloud Processing	ECCV 2018	Pytorch
Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes	SIGGRAPH Asia 2018
Learning Implicit Fields for Generative Shape Modeling	CVPR 2019	Tensorflow
Occupancy Networks: Learning 3D Reconstruction in Function Space	CVPR 2019	Pytorch
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation	CVPR 2019	Pytorch

结构光

结构光投影三维成像目前是机器人3D 视觉感知的主要方式,结构光成像系统是由若干个投影仪和 相机组成, 常用的结构形式有: 单投影仪-单相机、单投影仪-双相机 、单投影仪-多相机、单相机-双投影 仪和单相机-多投影仪等典型结构形式.

结构光投影三维成像的基本工作原理是：投影仪向目标物体投射特定的结构光照明图案,由相机摄取被目标调制后的图像,再通过图像处理和视觉模型求出目标物体的三维信息. 常用的投影仪主要有下列几种类型:液晶投影(LCD)、数字光调制投影(DLP)[如数字微镜器件 (DMD)]、激光 LED图案直接投影. 根据结构光投影次数划分,结构光投影三维成像可以分成单次投影3D和多次投影3D方法.

按照扫描方式又可分为：线扫描结构光、面阵结构光

参考链接：https://zhuanlan.zhihu.com/p/29971801

结构光三维表面成像：综述（一）

结构光三维表面成像：综述（二）

结构光三维表面成像：综述（三）

综述

Structured-light 3D surface imaging: a tutorial

机器人视觉三维成像技术综述

Real-time structured light profilometry a review

A state of the art in structured light patterns for surface profilometry

Phase shifting algorithms for fringe projection profilometry: a review

Overview of the 3D profilometry of phase shifting fringe projection

Temporal phase unwrapping algorithms for fringe projection profilometry:a comparative review

Lectures&Video

1. Build Your Own 3D Scanner: Optical Triangulation for Beginners
2. https://github.com/nikolaseu/thesis
3. CS6320 3D Computer Vision, Spring 2015

标定

1. 高效线结构光视觉测量系统标定方法
2. 一种新的线结构光标定方法
3. 一种结构光三维成像系统的简易标定方法
4. 基于单应性矩阵的线结构光系统简易标定方法
5. 线结构光标定方法综述
6. 三线结构光视觉传感器现场标定方法
7. 单摄像机单投影仪结构光三维测量系统标定方法
8. 超大尺度线结构光传感器内外参数同时标定
9. 单摄像机单投影仪结构光三维测量系统标定方法
10. 三维空间中线结构光与相机快速标定方法
11. 线结构光传感系统的快速标定方法

单次投影成像

单次投影结构光主要采用空间复用编码和频率复用编码形式实现 ,常用的编码形式有:彩色编码 、灰度索引、 几何形状编码和随机斑点. 目前在机器人手眼系统应用中,对于三维测量精度要求不高的场合,如码垛、拆垛、三维抓取等,比较受欢迎的是投射伪随机斑点获得目标三维信息 。

1. One-shot pattern projection for dense and accurate 3D acquisition in structured light
2. A single-shot structured light means by encoding both color and geometrical features
3. Dynamic 3D surface profilometry using a novel colour pattern encoded with a multiple triangular mode
4. Review of single-shot 3D shape measurement by phase calculation-based fringe projection techniques
5. Robust pattern decoding in shape-coded structured light

多次投影成像

多次投影3D方法主要采用时间复用编码方式实现,常用的图案编码形式有:二进制编码、多频相移编码和混合编码法(如格雷码＋相移条纹)等.

但是格雷码方法仅能在投射空间内进行离散的划分,空间分辨率受到成像器件的限制. 为了提高空间分辨率,需要增加投影条纹幅数,投射条纹宽度更小的格雷码条纹图,但条纹宽度过小会导致格雷码条纹的边缘效应,从而引 起解码误差.

正弦光栅条纹投影克服了格雷码空间离散划分的缺点,成为使用率最高的结构光类型之一. 众所周知,对于复杂外形,如有空洞、阶梯、遮挡等,采用正弦单频相移法条纹投影时,存在相位解包裹难题.另外为了能够从系列条纹图中求出相位绝对值,需要在条纹中插入特征点,比如一个点、一条线作为参考相位点,但是这些点或线特征标志有可能投影在物体的遮挡或阴影区域,或受到环境光等干扰等,发生丢失,影响测量结果的准确性. 因此,对于复杂轮廓的物体,常采用多频相移技术.

1. 三维重建的格雷码-相移光编码技术研究
2. Pattern codification strategies in structured light systems
3. Binary coded linear fringes for three-dimensional shape profiling
4. 3D shape measurement based on complementary Gray-code light
5. Phase shifting algorithms for fringe projection profilometry: a review
6. Overview of the 3D profilometry of phase shifting fringe projection
7. Temporal phase unwrapping algorithms for fringe projection profilometry:a comparative review
8. A multi-frequency inverse-phase error compensation method for projectornon linear in3D shape measurement

偏折法成像

对于粗糙表面,结构光可以直接投射到物体表面进行视觉成像测量;但对于大反射率光滑表面和镜面物体3D 测量,结构光投影不能直接投射到被测表面,3D测量还需要借助镜面偏折技术 .

1. Principles of shape from specular reflection
2. Deflectometry: 3D-metrology from nanometer to meter
3. Three-dimensional shape measurement of a highly reflected specular surface with structured light method
4. Three-dimensional shape measurements of specular objects using phase-measuring deflectometry

由于单次投影曝光和测量时间短,抗振动性能好,适合运动物体的3D测量,如机器人实时运动引导,手眼机器人对生产线上连续运动产品进行抓取等操作. 但深度垂直方向上的空间分辨率受到目标视场、镜头倍率和相机像素等因素的影响,大视场情况下不容易提升.

多次投影方法(如多频条纹方法)具有较高空间分辨率,能有效地解决表面斜率阶跃变化和空洞等难题. 不足之处在于:① 对于连续相移投影方法,3D重构的精度容易受到投影仪、相机的非线性和环境变化的影响;②抗振动性能差,不合适测量连续运动的物体;③在 Eye-in-Hand视觉导引系统中,机械臂不易在连续运动时进行3D成像和引导;④实时性差,不过随着投影仪投射频率和 CCD/CMOS图像传感器采集速度的提高,多次投影方法实时3D 成像的性能也在逐步改进.

偏折法对于复杂面型的测量,通常需要借助多次投影方法,因此具有多次投影方法相同的缺点.另外偏折法对曲率变化大的表面测量有一定的难度,因为条纹偏折后的反射角的变化率是被测表面曲率变化率的２倍,因此对被测物体表面的曲率变化比较敏感,很容易产生遮挡难题.

Other Papers

1. 基于面结构光的三维重建阴影补偿算法
2. Enhanced phase measurement profilometry for industrial 3D inspection automation
3. Profilometry of three-dimensional discontinuous solids by combining two-steps temporal phase unwrapping, co-phased profilometry and phase-shifting interferometry
4. 360-Degree Profilometry of Discontinuous Solids Co-Phasing ２-Projectors and１-Camera
5. Coherent digital demodulation of single-camera N-projections for 3D-object shape measurement Co-phased profilometr
6. High-speed 3D image acquisition using coded structured light projection
7. Accurate 3D measurement using a Structured Light System
8. Structured light stereoscopic imaging with dynamic pseudo-random patterns
9. Robust one-shot 3D scanning using loopy belief propagation
10. Robust Segmentation and Decoding of a Grid Pattern for Structured Light
11. Rapid shape acquisition using color structured light and multi-pass dynamic programming
12. Improved stripe matching for colour encoded structured light
13. Absolute phase mapping for one-shot dense pattern projection
14. 3D digital stereophotogrammetry: a practical guide to facial image acquisition
15. Method and apparatus for 3D imaging using light pattern having multiple sub-patterns
16. High speed laser three-dimensional imager
17. Three-dimensional dental imaging method and apparatus having a reflective member
18. 3D surface profile imaging method and apparatus using single spectral light condition
19. Three-dimensional surface profile imaging method and apparatus using single spectral light condition
20. High speed three dimensional imaging method
21. A hand-held photometric stereo camera for 3-D modeling
22. High-resolution, real-time 3D absolute coordinate measurement based on a phase-shifting method
23. A fast three-step phase shifting algorithm

Code

1. https://github.com/jakobwilm/slstudio
2. https://github.com/phreax/structured_light
3. https://github.com/nikolaseu/neuvision
4. https://github.com/pranavkantgaur/3dscan

扫描3D成像

扫描3D成像方法可分为扫描测距、主动三角法、色散共焦法等。扫描3D成像的最大优点是测量精度高,其中 色散共焦法还有其他方法难以比拟的优点,即非常适合测量透明物体、高反与光滑表面的物体. 但缺点是速度慢、效率低;当用于机械手臂末端时,可实现高精度3D测量,但不适合机械手臂实时3D引导与定位,因此应用场合有限;另外主动三角扫描在测量复杂结构形貌时容易产生遮挡,需要通过合理规划末端路径与姿态来解决.

扫描测距

扫描测距是利用一条准直光束通过一维测距扫描整个目标表面实现3D测量，主要包括：单点飞行时间法、激光散射干涉法、 共焦法。

单点测距扫描3D方法中,单点飞行时间法适合远距离扫描,测量精度较低,一般在毫米量级. 其他几种单点扫描方法有:单点激光干涉法、共焦法和单点激光主动三角法,测量精度较高,但前者对环境要求高;线扫描精度适中,效率高. 比较适合于机械手臂末端执行3D测量的应是主动激光三角法和色散共焦法

Paper

1. Active optical range imaging sensor
2. Active and passive range sensing for robotics

主动三角法

主动三角法是基于三角测量原理,利用准直光束、一条或多条平面光束扫描目标表面完成3D测量的. 光束常采用以下方式获得:激光准直、圆柱或二次曲面柱形棱角扩束,非相干光(如白光、LED 光源)通过小孔、狭缝(光栅)投影或相干光衍射等. 主动三角法可分为三种类型:单点扫描、单线扫描和多线扫描

Paper

1. Review of different 3D scanners and scanning techniques
2. 3D metrology using a collaborative robot with a laser triangulation sensor
3. Introductory review on Flying Triangulation a motion-robust optical 3D measurement principle
4. Flying triangulation an optical 3D sensor for the motion-robust acquisition of complex object
5. Hand-Guided 3D Surface Acquisition by Combining Simple Light Sectioning with Real-Time Algorithms

色彩共焦法

色散共焦似乎可以扫描测量粗糙和光滑的不透明和透明物体,如反射镜面、透明玻璃面等,目前在手机盖板三维检测等领域广受欢迎。色散共焦扫描有三种类型:单点一维绝对测距扫描、多点阵列扫描和连续线扫描。

Paper

1. Spectral characteristics of chromatic confocal imaging systems
2. Spectrally multiplexed chromatic confocal multipoint sensing
3. Chromatic confocal matrix sensor with actuated pinhole arrays
4. Multiplex acquisition approach for high speed 3d measurements with a chromatic confocal microscope
5. Fast 3D in line-sensor for specular and diffuse surfaces combining the chromatic confocal and triangulation principle
6. Single-shot depth-section imaging through chromatic slit-scan confocal microscopy
7. Three-dimensional surface profile measurement using a beam scanning chromatic confocal microscop

立体视觉3D成像

立体视觉字面意思是用一只眼睛或两只眼睛感知三维结构,一般情况下是指从不同的视点获取两 幅或多幅图像重构目标物体3D结构或深度信息. 深度感知视觉线索可分为 Monocular cues 和 Binocular cues(双目视差). 目前立体视觉3D 可以通过单目视觉、双目视觉、多 (目) 视觉、光场3D 成像(电子复眼或阵列相机)实现.

书籍

1. 机器视觉 Robot Vision

教程

1. 立体视觉书籍推荐&立体匹配十大概念综述---立体匹配算法介绍
2. 【关于立体视觉的一切】立体匹配成像算法BM，SGBM，GC，SAD一览
3. StereoVision--立体视觉（1）
4. StereoVision--立体视觉（2）
5. StereoVision--立体视觉（3）
6. StereoVision--立体视觉（4）
7. StereoVision--立体视觉（5）

综述

1. Review of Stereo Vision Algorithms: From Software to Hardware
2. 双目立体视觉的研究现状及进展
3. 双目视觉的匹配算法综述

单目视觉成像

单目视觉深度感知线索通常有:透视、焦距差异 、多视觉成像、覆盖、阴影 、运动视差等.

1. Depth map extracting based on geometric perspective an applicable２D to３D conversion technology
2. Focus cues affect perceived depth
3. 3D image acquisition system based on shape from focus technique
4. Multi-view stereo: a tutorial
5. 3D reconstruction from multiple images part1 principles
6. Three-dimensional reconstruction of hybrid surfaces using perspective shape from shading
7. Numerical methods for shape-from-shading a new survey with benchmarks
8. The neural basis of depth perception from motion parallax
9. Motion parallax in stereo 3D
10. 3D image sensor based on parallax motion

双目视觉

在机器视觉里利用两个相机从两个视点对同一个目标场景获取两个视点图像,再计算两个视点图像中同名点的视差获得目标场景的3D深度信息. 典型的双目立体视觉计算过程包含下面四个步骤:图像畸变矫正、立体图像对校正、图像配准和三角法重投影视差图计算.

双目视觉的难点：

1、光照敏感，被动光

2、双目视觉系统估计视差没那么容易，立体匹配是计算机视觉典型的难题，基线宽得到远目标测距准，而基线短得到近目标测距结果好。谈到双目系统的难点，除了立体匹配，还有标定。标定后的系统会出现“漂移”的，所以在线标定是必须具有的。

综述

1. 双目立体视觉匹配技术综述

视差和深度计算

1. Real-time depth computation using stereo imaging
2. Binocular disparity and the perception of depth
3. Fast Stereo Disparity Maps Refinement By Fusion of Data-Based And Model-Based Estimations

立体匹配

匹配方法分两种，全局法和局部法，实用的基本是局部法，因为全局法太慢。

（一）基于全局约束的立体匹配算法：在本质上属于优化算法，它是将立体匹配问题转化为寻找全局能量函数的最优化问题，其代表算法主要有图割算法、置信度传播算法和协同优化算法等．全局算法能够获得较低的总误匹配率，但算法复杂度较高,很难满足实时的需求，不利于在实际工程中使用，常见的算法有DP、BP 等。

（二）基于局部约束的立体匹配算法：主要是利用匹配点周围的局部信息进行计算，由于其涉及到的信息量较少，匹配时间较短，因此受到了广泛关注，其代表算法主要有 SAD、SSD、ZSAD、NCC等。

1. DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
2. Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
3. PMSC: PatchMatch-Based Superpixel Cut for Accurate Stereo Matching
4. Exact Bias Correction and Covariance Estimation for Stereo Vision
5. Efficient minimal-surface regularization of perspective depth maps in variational stereo
6. Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision
7. Leveraging Stereo Matching with Learning-based Confidence Measures
8. Graph Cut based Continuous Stereo Matching using Locally Shared Labels
9. Cross-Scale Cost Aggregation for Stereo Matching
10. Fast Cost-Volume Filtering for Visual Correspondence and Beyond
11. Constant Time Weighted Median Filtering for Stereo Matching and Beyond
12. [A non-local cost aggregation method for stereo matching](http://fcv2011.ulsan.ac.kr/files/announcement/592/A Non-Local Aggregation Method Stereo Matching.pdf)
13. On building an accurate stereo matching system on graphics hardware
14. Efficient large-scale stereo matching
15. Accurate, dense, and robust multiview stereopsis
16. A constant-space belief propagation algorithm for stereo matching
17. Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling
18. Cost aggregation and occlusion handling with WLS in stereo matching
19. Stereo matching: An outlier confidence approach
20. A region based stereo matching algorithm using cooperative optimization
21. Multi-view stereo for community photo collections
22. A performance study on different cost aggregation approaches used in real-time stereo matching
23. Evaluation of cost functions for stereo matching
24. Adaptive support-weight approach for correspondence search
25. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure

多目视觉

多(目)视觉成像,也称多视点立体成像,用单个或多个相机从多个视点获取同一个目标场景的多幅图像,重构目标场景的三维信息.

1. Adaptive structure from motion with a contrario model estimation
2. A comparison and evaluation of multi-view stereo reconstruction algorithms
3. Multiple view geometry in computer vision

光场成像

光场3D成像的原理与传统 CCD和 CMOS相机成像原理在结构原理上有所差异,传统相机成像是光线穿过镜头在后续的成像平面上直接成像,一般是2D图像;光场相机成像是在传感器平面前增加了一个微透镜阵列,将经过主镜头入射的光线再次穿过每个微透镜,由感光阵列接收,从而获得光线的方向与位置信息,使成像结果可在后期处理,达到先拍照,后聚焦的效果.

光场相机的优点是:单个相机可以进行3D成像,横向和深度方向的空间分辨率可以达到20μm到 mm 量级,景深比普通相机大好几倍,比较适合Eye-in-Hand系统3D测量与引导,但目前精度适中的商业化光场相机价格昂贵.

1. Light field imaging models calibrations reconstructions and applications
2. Extracting depth information from stereo vision system using a correlation and a feature based methods
3. 基于微透镜阵列型光场相机的多目标快速测距方法
4. 基于光场相机的四维光场图像水印及质量评价
5. 基于光场相机的深度面光场计算重构
6. 光场相机视觉测量误差分析
7. 一种基于光场图像的聚焦光场相机标定方法
8. 光场相机成像模型及参数标定方法综述

SFM

Structure from Motion（SfM）是一个估计相机参数及三维点位置的问题。一个基本的SfM pipeline可以描述为:对每张2维图片检测特征点（feature point），对每对图片中的特征点进行匹配，只保留满足几何约束的匹配，最后执行一个迭代式的、鲁棒的SfM方法来恢复摄像机的内参（intrinsic parameter）和外参(extrinsic parameter)。并由三角化得到三维点坐标，然后使用Bundle Adjustment进行优化。

SFM（Structure From Motion），主要基于多视觉几何原理，用于从运动中实现3D重建，也就是从无时间序列的2D图像中推算三维信息，是计算机视觉学科的重要分支。

使用同一相机在其内参数不变的条件下,从不同视点获取多幅图像,重构目标场景的三维信息. 该技术常用 于跟踪目标场景中大量的控制点,连续恢复场景3D结构信息、相机的姿态和位置.

SfM方法可以分为增量式（incremental/sequential SfM）,全局式（global SfM），混合式（hybrid SfM）,层次式（hierarchica SfM）。另外有基于语义的SfM(Semantic SfM)和基于Deep learning的SfM。

Incremental SfM

Global SfM

Hierarchical SfM

Multi-Stage SfM

Non Rigid SfM

参考

基于单目视觉的三维重建算法综述

Turtorial

1. Open Source Structure-from-Motion. M. Leotta, S. Agarwal, F. Dellaert, P. Moulon, V. Rabaud. CVPR 2015 Tutorial (material).
2. Large-scale 3D Reconstruction from Images](https://home.cse.ust.hk/~tshenaa/sub/ACCV2016/ACCV_2016_Tutorial.html). T. Shen, J. Wang, T.Fang, L. Quan. ACCV 2016 Tutorial.

Incremental SfM

增量式SfM首先使用SIFT特征检测器提取特征点并计算特征点对应的描述子（descriptor），然后使用ANN（approximate nearest neighbor）方法进行匹配，低于某个匹配数阈值的匹配对将会被移除。对于保留下来的匹配对，使用RANSAC和八点法来估计基本矩阵（fundamental matrix），在估计基本矩阵时被判定为外点（outlier）的匹配被看作是错误的匹配而被移除。对于满足以上几何约束的匹配对，将被合并为tracks。然后通过incremental方式的SfM方法来恢复场景结构。首先需要选择一对好的初始匹配对，一对好的初始匹配对应该满足：

（1）足够多的匹配点；

（2）宽基线。之后增量式地增加摄像机，估计摄像机的内外参并由三角化得到三维点坐标，然后使用Bundle Adjustment进行优化。

增量式SfM从无序图像集合计算三维重建的常用方法，增量式SfM可分为如图 3所示几个阶段：图像特征提取、特征匹配、几何约束、重建初始化、图像注册、三角化、outlier过滤、Bundle adjustment等步骤。

增量式SfM优势：系统对于特征匹配以及外极几何关系的外点比较鲁棒，重讲场景精度高；标定过程中通过RANSAC不断过滤外点；捆绑调整不断地优化场景结构。

增量式SfM缺点：对初始图像对选择及摄像机的添加顺序敏感；场景漂移，大场景重建时的累计误差。效率不足，反复的捆绑调整需要大量的计算时间。

实现增量式SfM框架的包含COLMAP、openMVG、Theia等

1. Photo Tourism: Exploring Photo Collections in 3D. N. Snavely, S. M. Seitz, and R. Szeliski. SIGGRAPH 2006.
2. Towards linear-time incremental structure from motion. C. Wu. 3DV 2013.
3. Structure-from-Motion Revisited. Schöenberger, Frahm. CVPR 2016.

Global SfM

全局式：估计所有摄像机的旋转矩阵和位置并三角化初始场景点。

优势：将误差均匀分布在外极几何图上，没有累计误差。不需要考虑初始图像和图像添加顺序的问题。仅执行一次捆绑调整，重建效率高。

缺点：鲁棒性不足，旋转矩阵求解时L1范数对外点相对鲁棒，而摄像机位置求解时相对平移关系对匹配外点比较敏感。场景完整性，过滤外极几何边，可能丢失部分图像。

1. Combining two-view constraints for motion estimation V. M. Govindu. CVPR, 2001.
2. Lie-algebraic averaging for globally consistent motion estimation. V. M. Govindu. CVPR, 2004.
3. Robust rotation and translation estimation in multiview reconstruction. D. Martinec and T. Pajdla. CVPR, 2007.
4. Non-sequential structure from motion. O. Enqvist, F. Kahl, and C. Olsson. ICCV OMNIVIS Workshops 2011.
5. Global motion estimation from point matches. M. Arie-Nachimson, S. Z. Kovalsky, I. KemelmacherShlizerman, A. Singer, and R. Basri. 3DIMPVT 2012.
6. Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion. P. Moulon, P. Monasse and R. Marlet. ICCV 2013.
7. A Global Linear Method for Camera Pose Registration. N. Jiang, Z. Cui, P. Tan. ICCV 2013.
8. Global Structure-from-Motion by Similarity Averaging. Z. Cui, P. Tan. ICCV 2015.
9. Linear Global Translation Estimation from Feature Tracks Z. Cui, N. Jiang, C. Tang, P. Tan, BMVC 2015.

混合式

混合式SfM[5]在一定程度上综合了incremental SfM和global SfM各自的优点。HSfM的整个pipeline可以概括为全局估计摄像机旋转矩阵，增量估计摄像机位置，三角化初始场景点。

用全局的方式提出一种基于社区的旋转误差平均法，该方法既考虑了对极几何的精度又考虑了成对几何的精度。基于已经估计的相机的绝对旋转姿态，用一种增量的方式估计相机光心位置。对每个添加的相机，其旋转和内参保持不变，同时使用改进的BA细化光心和场景结构。

层次式SfM同样借鉴incremental SfM和global SfM各自优势，但是基于分段式的incremental SfM和全局式SfM，没有像混合式SfM分成两个阶段进行。

SfM中我们用来做重建的点是由特征匹配提供的，所以SfM获得特征点的方式决定了它不可能直接生成密集点云。而MVS则几乎对照片中的每个像素点都进行匹配，几乎重建每一个像素点的三维坐标，这样得到的点的密集程度可以较接近图像为我们展示出的清晰度。

Hierarchical SfM

1. Structure-and-Motion Pipeline on a Hierarchical Cluster Tree. A. M.Farenzena, A.Fusiello, R. Gherardi. Workshop on 3-D Digital Imaging and Modeling, 2009.
2. Randomized Structure from Motion Based on Atomic 3D Models from Camera Triplets. M. Havlena, A. Torii, J. Knopp, and T. Pajdla. CVPR 2009.
3. Efficient Structure from Motion by Graph Optimization. M. Havlena, A. Torii, and T. Pajdla. ECCV 2010.
4. Hierarchical structure-and-motion recovery from uncalibrated images. Toldo, R., Gherardi, R., Farenzena, M. and Fusiello, A.. CVIU 2015.

Multi-Stage SfM

1. Parallel Structure from Motion from Local Increment to Global Averaging. S. Zhu, T. Shen, L. Zhou, R. Zhang, J. Wang, T. Fang, L. Quan. arXiv 2017.
2. Multistage SFM : Revisiting Incremental Structure from Motion. R. Shah, A. Deshpande, P. J. Narayanan. 3DV 2014. -> Multistage SFM: A Coarse-to-Fine Approach for 3D Reconstruction, arXiv 2016.
3. HSfM: Hybrid Structure-from-Motion. H. Cui, X. Gao, S. Shen and Z. Hu, ICCV 2017.

Non Rigid SfM

1. Robust Structure from Motion in the Presence of Outliers and Missing Data. G. Wang, J. S. Zelek, J. Wu, R. Bajcsy. 2016.

Project&code

Project	Language	License
Bundler	C++	GNU General Public License - contamination
Colmap	C++	BSD 3-clause license - Permissive
TeleSculptor	C++	BSD 3-Clause license - Permissive
MicMac	C++	CeCILL-B
MVE	C++	BSD 3-Clause license + parts under the GPL 3 license
OpenMVG	C++	MPL2 - Permissive
OpenSfM	Python	Simplified BSD license - Permissive
TheiaSfM	C++	New BSD license - Permissive

TOF

飞行时间 (TOF) 相机每个像素利用光飞行的时间差来获取物体的深度。TOF成像可用于大视野、远距离、低精度、低成本的3D图像采集. 其特点是:检测速度快、视野范围较大、工作距离远、价格便宜,但精度低,易受环境 光的干扰 。

分类

直接TOF

D-TOF通常用于单点测距系统, 为了实现面积范围3D成像,通常需要采用扫描技术 。

间接TOF

间接 TOF(I-TOF),时间往返行程是从光强度的时间选通测量中间接外推获得 ，I-TOF不需要精确的 计时,而是采用时间选通光子计数器或电荷积分器,它们可以在像素级实现.

教程

1. ToF技术是什么？和结构光技术又有何区别?
2. 3D相机--TOF相机

Paper

1. https://arxiv.org/pdf/1511.07212.pdf)

Multi-view Stereo

多视角立体视觉（Multiple View Stereo，MVS）是对立体视觉的推广，能够在多个视角（从外向里）观察和获取景物的图像，并以此完成匹配和深度估计。某种意义上讲，SLAM/SFM其实和MVS是类似的，只是前者是摄像头运动，后者是多个摄像头视角（可以是单相机的多个视角图像，也可以是多相机的多视角图像）。也可以说，前者可以在环境里面“穿行”，而后者更像在环境外“旁观”。

多视角立体视觉的pipelines如下：

1. 收集图像；
2. 针对每个图像计算相机参数；
3. 从图像集和相应的摄像机参数重建场景的3D几何图形；
4. 可选择地重建场景的形状和纹理颜色。

参考链接：多视角立体视觉MVS简介

paper

1. Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume
2. Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
3. Point-Based Multi-View Stereo Network
4. Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
5. NRMVS: Non-Rigid Multi-View Stereo
6. Multi-View Stereo 3D Edge Reconstruction
7. Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
8. Multi-View Stereo for Community Photo Collections
9. Multi-View Stereo: A Tutorial
10. State of the Art 3D Reconstruction Techniques

综述

1. Multi-view stereo: A tutorial
2. State of the Art 3D Reconstruction Techniques N. Snavely, Y. Furukawa, CVPR 2014 tutorial slides. Introduction MVS with priors - Large scale MVS

Point cloud computation（点云计算）

1. Accurate, Dense, and Robust Multiview Stereopsis. Y. Furukawa, J. Ponce. CVPR 2007. PAMI 2010
2. State of the art in high density image matching. F. Remondino, M.G. Spera, E. Nocerino, F. Menna, F. Nex . The Photogrammetric Record 29(146), 2014.
3. Progressive prioritized multi-view stereo. A. Locher, M. Perdoch and L. Van Gool. CVPR 2016.
4. Pixelwise View Selection for Unstructured Multi-View Stereo. J. L. Schönberger, E. Zheng, M. Pollefeys, J.-M. Frahm. ECCV 2016.
5. TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. A. Romanoni, M. Matteucci. ICCV 2019

Surface computation & refinements（曲面计算与优化）

1. Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts. P. Labatut, J-P. Pons, R. Keriven. ICCV 2007
2. Multi-View Stereo via Graph Cuts on the Dual of an Adaptive Tetrahedral Mesh. S. N. Sinha, P. Mordohai and M. Pollefeys. ICCV 2007.
3. Towards high-resolution large-scale multi-view stereo. H.-H. Vu, P. Labatut, J.-P. Pons, R. Keriven. CVPR 2009.
4. Refinement of Surface Mesh for Accurate Multi-View Reconstruction. R. Tylecek and R. Sara. IJVR 2010.
5. High Accuracy and Visibility-Consistent Dense Multiview Stereo. H.-H. Vu, P. Labatut, J.-P. Pons, R. Keriven. Pami 2012.
6. Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces M. Jancosek et al. 2014.
7. A New Variational Framework for Multiview Surface Reconstruction. B. Semerjian. ECCV 2014.
8. Photometric Bundle Adjustment for Dense Multi-View 3D Modeling. A. Delaunoy, M. Pollefeys. CVPR2014.
9. Global, Dense Multiscale Reconstruction for a Billion Points. B. Ummenhofer, T. Brox. ICCV 2015.
10. Efficient Multi-view Surface Refinement with Adaptive Resolution Control. S. Li, S. Yu Siu, T. Fang, L. Quan. ECCV 2016.
11. Multi-View Inverse Rendering under Arbitrary Illumination and Albedo, K. Kim, A. Torii, M. Okutomi, ECCV2016.
12. Shading-aware Multi-view Stereo, F. Langguth and K. Sunkavalli and S. Hadap and M. Goesele, ECCV 2016.
13. Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity, C. Mostegel, R. Prettenthaler, F. Fraundorfer and H. Bischof. CVPR 2017.
14. Multi-View Stereo with Single-View Semantic Mesh Refinement, A. Romanoni, M. Ciccone, F. Visin, M. Matteucci. ICCVW 2017

Machine Learning based MVS

1. Matchnet: Unifying feature and metric learning for patch-based matching, X. Han, Thomas Leung, Y. Jia, R. Sukthankar, A. C. Berg. CVPR 2015.
2. Stereo matching by training a convolutional neural network to compare image patches, J., Zbontar, and Y. LeCun. JMLR 2016.
3. Efficient deep learning for stereo matching, W. Luo, A. G. Schwing, R. Urtasun. CVPR 2016.
4. Learning a multi-view stereo machine, A. Kar, C. Häne, J. Malik. NIPS 2017.
5. Learned multi-patch similarity, W. Hartmann, S. Galliani, M. Havlena, L. V. Gool, K. Schindler.I CCV 2017.
6. Surfacenet: An end-to-end 3d neural network for multiview stereopsis, Ji, M., Gall, J., Zheng, H., Liu, Y., Fang, L. ICCV2017.
7. DeepMVS: Learning Multi-View Stereopsis, Huang, P. and Matzen, K. and Kopf, J. and Ahuja, N. and Huang, J. CVPR 2018.
8. RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials, D. Paschalidou and A. O. Ulusoy and C. Schmitt and L. Gool and A. Geiger. CVPR 2018.
9. MVSNet: Depth Inference for Unstructured Multi-view Stereo, Y. Yao, Z. Luo, S. Li, T. Fang, L. Quan. ECCV 2018.
10. Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency, T. Khot, S. Agrawal, S. Tulsiani, C. Mertz, S. Lucey, M. Hebert. 2019.
11. DPSNET: END-TO-END DEEP PLANE SWEEP STEREO, Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon. 2019.
12. Point-based Multi-view Stereo Network, Rui Chen, Songfang Han, Jing Xu, Hao Su. ICCV 2019.

Multiple View Mesh Texturing（多视图网格纹理）

1. Seamless image-based texture atlases using multi-band blending. C. Allène, J-P. Pons and R. Keriven. ICPR 2008.
2. Let There Be Color! - Large-Scale Texturing of 3D Reconstructions. M. Waechter, N. Moehrle, M. Goesele. ECCV 2014

Texture Mapping(纹理贴图)

1. 3D Textured Model Encryption via 3D Lu Chaotic Mapping

大场景三维重建

1. Large-scale 3D Reconstruction from Images
2. Graphmatch: Efficient Large-Scale Graph Construction for Structure from Motion

深度学习方式的三维重建

1. Surfacenet: An end-to-end 3d neural network for multiview stereopsis
2. Stereo matching by training a convolutional neural network to compare image patches
3. AtlasNet: A Papier-Mache Approach to Learning 3D Surface Generation

Courses

• Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
• Computational Photography - Alexei A. Efros (CMU)
• Computational Photography - Derek Hoiem (UIUC)
• Computational Photography - James Hays (Brown University)
• Digital & Computational Photography - Fredo Durand (MIT)
• Computational Camera and Photography - Ramesh Raskar (MIT Media Lab)
• Computational Photography - Irfan Essa (Georgia Tech)
• Courses in Graphics - Stanford University
• Computational Photography - Rob Fergus (NYU)
• Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
• Computational Photography - Kyros Kutulakos (University of Toronto)
• Computer Vision for Visual Effects - Rich Radke (Rensselaer Polytechnic Institute)
• Introduction to Image Processing - Rich Radke (Rensselaer Polytechnic Institute)

Software

• MATLAB Functions for Multiple View Geometry
• Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
• OpenGV - geometric computer vision algorithms
• MinimalSolvers - Minimal problems solver
• Multi-View Environment
• Visual SFM
• Bundler SFM
• openMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwares
• Patch-based Multi-view Stereo V2
• Clustering Views for Multi-view Stereo
• Floating Scale Surface Reconstruction
• Large-Scale Texturing of 3D Reconstructions
• Multi-View Stereo Reconstruction

Project&code

Project	Language	License
Colmap	C++ CUDA	BSD 3-clause license - Permissive (Can use CGAL -> GNU General Public License - contamination)
GPUIma + fusibile	C++ CUDA	GNU General Public License - contamination
HPMVS	C++	GNU General Public License - contamination
MICMAC	C++	CeCILL-B
MVE	C++	BSD 3-Clause license + parts under the GPL 3 license
OpenMVS	C++ (CUDA optional)	AGPL3
PMVS	C++ CUDA	GNU General Public License - contamination
SMVS Shading-aware Multi-view Stereo	C++	BSD-3-Clause license

深度相机三维重建

主要是基于Kinect这类深度相机进行三维重建，包括KinectFusion、Kintinuous，ElasticFusion、InfiniTAM，BundleFusion

papers

1. State of Art on 3D Reconstruction with RGB-D Cameras
2. 3D indoor scene modeling from RGB-D data: a survey

手机移动端的三维重建

1. 3DCapture: 3D Reconstruction for a Smartphone

基于线条/面的三维重建

1. Surface Reconstruction from 3D Line Segments

Planar Reconstruction

参考：https://github.com/BigTeacher-777/Awesome-Planar-Reconstruction

Papers

• [PlaneRCNN] PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image [CVPR2019(Oral)][Pytorch]
• [PlanarReconstruction] Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding [CVPR2019][Pytorch]
• [Planerecover] Recovering 3D Planes from a Single Image via Convolutional Neural Networks [ECCV2018][Tensorflow]
• [PlaneNet] PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image [CVPR2018][Tensorflow]

Datasets

• ScanNet Dataset (PlaneNet) [Train][Test]
• ScanNet Dataset (PlaneRCNN)[Link]
• NYU Depth Dataset [Link]
• SYNTHIA Dataset [Link]

3D人脸重建

1、Nonlinear 3D Face Morphable Model

2、On Learning 3D Face Morphable Model from In-the-wild Images

3、Cascaded Regressor based 3D Face Reconstruction from a Single Arbitrary View Image

4、JointFace Alignment and 3D Face Reconstruction

5、Photo-Realistic Facial Details Synthesis From Single Image

6、FML: Face Model Learning from Videos

7、Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric

8、Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

9、Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning

10、Face Alignment Across Large Poses: A 3D Solution

纹理/材料分析与合成

1. Texture Synthesis Using Convolutional Neural Networks (2015)[Paper]
2. Two-Shot SVBRDF Capture for Stationary Materials (SIGGRAPH 2015) [Paper]
3. Reflectance Modeling by Neural Texture Synthesis (2016) [Paper]
4. Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks (2017)[Paper]
5. High-Resolution Multi-Scale Neural Texture Synthesis (2017) [Paper]
6. Reflectance and Natural Illumination from Single Material Specular Objects Using Deep Learning (2017) [Paper]
7. Joint Material and Illumination Estimation from Photo Sets in the Wild (2017) [Paper]
8. TextureGAN: Controlling Deep Image Synthesis with Texture Patches (2018 CVPR) [Paper]
9. Gaussian Material Synthesis (2018 SIGGRAPH) [Paper]
10. Non-stationary Texture Synthesis by Adversarial Expansion (2018 SIGGRAPH) [Paper]
11. Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients (2018 CVPR) [Paper]
12. LIME: Live Intrinsic Material Estimation (2018 CVPR) [Paper]
13. Learning Material-Aware Local Descriptors for 3D Shapes (2018) [Paper]

场景合成/重建

1. Make It Home: Automatic Optimization of Furniture Arrangement (2011, SIGGRAPH) [Paper]
2. Interactive Furniture Layout Using Interior Design Guidelines (2011) [Paper]
3. Synthesizing Open Worlds with Constraints using Locally Annealed Reversible Jump MCMC (2012) [Paper]
4. Example-based Synthesis of 3D Object Arrangements (2012 SIGGRAPH Asia) [Paper]
5. Sketch2Scene: Sketch-based Co-retrieval and Co-placement of 3D Models (2013) [Paper]
6. Action-Driven 3D Indoor Scene Evolution (2016) [Paper]
7. The Clutterpalette: An Interactive Tool for Detailing Indoor Scenes (2015) [Paper]
8. Relationship Templates for Creating Scene Variations (2016) [Paper]
9. IM2CAD (2017) [Paper]
10. Predicting Complete 3D Models of Indoor Scenes (2017) [Paper]
11. Complete 3D Scene Parsing from Single RGBD Image (2017) [Paper]
12. Adaptive Synthesis of Indoor Scenes via Activity-Associated Object Relation Graphs (2017 SIGGRAPH Asia) [Paper]
13. Automated Interior Design Using a Genetic Algorithm (2017) [Paper]
14. SceneSuggest: Context-driven 3D Scene Design (2017) [Paper]
15. A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition (2017)[Paper]
16. Human-centric Indoor Scene Synthesis Using Stochastic Grammar (2018, CVPR)[Paper] [Supplementary] [Code]
17. FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans (2018) [Paper] [Code]
18. ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2018) [Paper]
19. Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars (2018) [Paper]
20. Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image (ECCV 2018) [Paper]
21. Automatic 3D Indoor Scene Modeling from Single Panorama (2018 CVPR) [Paper]
22. Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding (2019 CVPR) [Paper] [Code]
23. 3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers (ICCV 2019) [Paper]