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hty@hty-ThinkStation-P330:~/Desktop/work/test$ DensifyPointCloud -i sfm.txt
16:01:04 [App ] Build date: Jul 19 2021, 14:56:48
16:01:04 [App ] CPU: Intel(R) Xeon(R) E-2244G CPU @ 3.80GHz (8 cores)
16:01:04 [App ] RAM: 31.20GB Physical Memory 2.00GB Virtual Memory
16:01:04 [App ] OS: Linux 5.4.0-72-generic (x86_64)
16:01:04 [App ] SSE & AVX compatible CPU & OS detected
16:01:04 [App ] Command line: -i sfm.txt
16:01:04 [App ] error: invalid project

工作空间目录结构：
test
----images(包含orbslam2运行时读入的所有image)
----sfm.txt

请问如何运行DensifyPointCloud指令呢

Hi @WangWen-Believer,

I extend your implementation from ORB-SLAM2 to ORB-SLAM3. However, when running OpenMVS as you suggested (readpose files + modified Densify Interface), I encountered an error regarding: malloc(): invalid size (unsorted), as described below:

We are using Keyframe from SLAM to achieve 3D reconstruction
load mvs ok
numViews 304
deal with feature points
13:03:05 [App ] Set the ROI by the estimated core points
13:03:05 [App ] Point-cloud composed of 11902 points with:

points info:
11901 points inside ROI (99.99%)
inside ROI track length: 0 min / 7.48256 mean (5.24675 std) / 70 max
outside ROI track length: 8 min / 8 mean (0 std) / 8 max
visibility info (89058 views - 7.48 views/point):
4 points with 1- views (0.03%)
59 points with 2 views (0.50%)
1804 points with 3 views (15.16%)
10035 points with 4+ views (84.31%)
0 min / 7.48261 mean (5.24653 std) / 70 max
13:03:06 [App ] Preparing2 images for dense reconstruction completed: 304 images (474ms)
malloc(): invalid size (unsorted)
Aborted

//  保存地图点
void Map::SaveMapPoint(ofstream &f, MapPoint *mp)
{
    //保存当前MapPoint世界坐标值
    cv::Mat mpWorldPos = mp->GetWorldPos();
    f <<" " <<mpWorldPos.at<float>(0)<<" " << mpWorldPos.at<float>(1)<<" " << mpWorldPos.at<float>(2) << " ";
    f << (mp->nObs)/2<< " ";

    std::map<KeyFrame*,size_t> mapObservation = mp->GetObservations();
    for(auto mit = mapObservation.begin(); mit != mapObservation.end(); mit++)
    {
        int Frameid;
        Frameid = mit->first->mnId;
        auto keyid = find(KeyId.begin(),KeyId.end(),Frameid) - KeyId.begin();
        f << keyid << " ";
    }
    f << "\n";
}

请问大佬往sfm.txt当中写这个点被哪些关键字观测到时，为什么要除2呀

在单目模式下添加了SLAM.SaveMap("../Examples/output/sfm.txt",im.size);
但是在目录下没有输出,有没有成功的,可以交流一下.

openmvs导入orbslam2生成的sfm.txt的时候，[app]显示select 0 images，请问这个是问题出在哪了呀？

你好，使用orbslam2跑出sfm.txt后，放入openmvs中运行， 出现了这个问题。

17:19:34 [App     ] CPU: Intel(R) Core(TM) i5-8400 CPU @ 2.80GHz (6 cores)
17:19:34 [App     ] RAM: 15.51GB Physical Memory 0B Virtual Memory
17:19:34 [App     ] OS: Linux 5.10.0-8-generic (x86_64)
17:19:34 [App     ] Disk: 23.68GB (78.24GB) space
17:19:34 [App     ] SSE & AVX compatible CPU & OS detected
17:19:34 [App     ] Command line: -w /home/frans/code/data/npu_image -i sfm.txt -o dense.mvs
We are using Keyframe from SLAM to achieve 3D reconstruction
===========read_mvs_pose========
===========save_pointcloud_obj========
===========save_pointcloud_obj========
load mvs ok 
numViews 204
deal with feature points
=====================load_scene==================end======
17:19:35 [App     ] Preparing images for dense reconstruction completed: 204 images (572ms)
17:19:35 [App     ] error: reference image  56 has not enough images in view
···
17:19:37 [App     ] error: reference image 146 has not enough images in view
17:19:37 [App     ] error: reference image  67 has not enough images in view
17:19:37 [App     ] error: reference image 139 has not enough images in view
···
17:19:38 [App     ] error: reference image 120 has not enough images in view
17:19:38 [App     ] error: reference image  39 has not enough images in view
17:19:38 [App     ] error: reference image 174 has not enough images in view
17:19:38 [App     ] Selecting images for dense reconstruction completed: 0 images (3s156ms)
Fused depth-maps 0 (100%, 0ms)
17:19:38 [App     ] Depth-maps fused and filtered: 0 depth-maps, 0 depths, 0 points (-2147483648%%) (0ms)
17:19:38 [App     ] Densifying point-cloud completed: 0 points (3s812ms)
17:19:38 [App     ] Scene saved (1ms):
	204 images (204 calibrated)
	0 points, 0 vertices, 0 faces
17:19:38 [App     ] MEMORYINFO: {
17:19:38 [App     ] 	VmPeak:	 1368416 kB
17:19:38 [App     ] 	VmSize:	 1302880 kB
17:19:38 [App     ] } ENDINFO

这个问题是点云太少了吗？谢谢解答。

您好，我像使用orbslam2和openmvs扫描出机器人附近的环境，最终将其导入模拟器中为机器人避障提供参考，想要了解一下openmvs生成的网格模型的坐标系在哪里？

jie@jie:~/0reconstruction/orb_mvs/openMVS/data$ ./DensifyPointCloud -w /home/jie/0reconstruction/orb_mvs/openMVS/data/ -i sfm.txt -o dense.mvs
10:53:42 [App ] Build date: Jan 11 2023, 21:54:35
10:53:42 [App ] CPU: Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz (8 cores)
10:53:42 [App ] RAM: 7.68GB Physical Memory 9.54GB Virtual Memory
10:53:42 [App ] OS: Linux 5.4.0-132-generic (x86_64)
10:53:42 [App ] SSE & AVX compatible CPU & OS detected
10:53:42 [App ] Command line: -w /home/jie/0reconstruction/orb_mvs/openMVS/data/ -i sfm.txt -o dense.mvs
10:53:42 [App ] error: invalid project

我的文件目录为：
openMVS
---data
---------image-----里面放了TUM的rgbd数据集的rgb图片
---------sfm.txt

我参考了博主在另一个回答下面回复：
你这里的运行指令不对啊，我就是为了最大程度不更改源作者代码的运行操作，你只需要把图像放入到 * */data/image文件夹里，将sfm.txt拷贝到 * * /data这个工作环境目录中，DensifyPointCloud -w * */data -i sfm.txt -o dense.mvs就可以了

请问ARkit + OpenMVS三维重建博主搞过吗？我用ORBSLAM保存的位姿 + OpenMVS实现了三维重建，但ARKit应该也是个SLAM算法，但是ARkit得到的位姿和ORBSLAM的好像不太一样，后续的建图貌似还是有问题的。UP有研究过这块吗？

作者您好，我想要制作自己的mvsnet数据集，请问一下您的代码能用RGB-D相机获取可用的MVSNet的位姿吗？

/libs/MVS/../Common/List.h:369: TYPE& SEACAVE::cList<TYPE, ARG_TYPE, useConstruct, grow, IDX_TYPE>::operator[](SEACAVE::cList<TYPE, ARG_TYPE, useConstruct, grow, IDX_TYPE>::IDX) [with TYPE = MVS::Image; ARG_TYPE = const MVS::Image&; int useConstruct = 2; int grow = 16; IDX_TYPE = unsigned int; SEACAVE::cList<TYPE, ARG_TYPE, useConstruct, grow, IDX_TYPE>::IDX = unsigned int]: Assertion `index < _size' failed.