This blog post is my learning notes for learning 3D reconstruction. Most of the content of this blog post comes from various tutorial blogs on the Internet. This blog post is only for my own learning records.

theoretical study

Visual 3D reconstruction = positioning and pose + dense reconstruction + surface reconstruction/mesh + texture map

The difference between SLAM and 3D reconstruction ( What is the difference between SLAM and 3D reconstruction? - Know ):

sfm corresponds to an unordered image, while SLAM is generally ordered
sfm does not require calibration, while SLAM generally requires
sfm is offline, SLAM is online

Application Scenarios of 3D Reconstruction

colmap is the current state-of-the-art sfm and mvs framework. The 3D reconstruction introduced in this blog post is mainly the colmap framework

front end

shift feature detection and description

rear end

sfm combat

MVS algorithm

3D reconstruction actual combat colmap combined with opoenmvs

TSDF （truncated signed distance function）

If you get the following information:

Original picture (multi-view)
Corresponding depth information
camera pose
camera internal reference

The process of updating and combining TSDF calculates the TSDF value of each voxel step by step, and then stitches them together.

The so-called voxels are small squares one by one, which can eventually be combined into a 3D reconstruction result. As shown below. The result of 3D reconstruction will be put into a cube, and this cube is composed of voxels. Proceed as follows:

Create a large empty area (volume)
This area consists of many small areas, each called voxel
Need to calculate the TSDF value of each voxel and its weight

As shown below. T is the meaning of truncation, which is set to a fixed value if it is too far or too close. The SDF value of a voxel is its distance to the nearest surface. Let a certain point X be one of the voxels, and the green line is the closest plane to it, and point P is the closest point to X on the plane, and the distance between them is SDF.

For truncated T, the closer to 0, the closer, and the closer to plus or minus 1, the farther away. positive and negative front and rear