What is the smallest three-dimensional space unit of a real scene?

In the field of real 3D reconstruction, the smallest spatial unit usually refers to a voxel. Voxels are volume pixels in three-dimensional space, similar to pixels in two-dimensional images. Each voxel represents a discrete unit in space and may contain information about the spatial unit.

In 3D reconstruction, voxels are usually used to represent the volume of a scene or an object. These voxels can form a three-dimensional grid and a discrete representation, which is more convenient for the computer to process and analyze this three-dimensional scene. The size of the smallest spatial unit (voxel) in 3D reconstruction of real scene depends on the specific application and system requirements. Choosing the appropriate voxel size depends not only on the details of the scene, but also on finding a balance between computing and storage efficiency. Smaller voxels can provide better reconstruction results, but they also require more computing resources.

Generally speaking, the smallest spatial unit in real 3D reconstruction refers to the voxels used to represent and process 3D scenes.

Methods of 3D reconstruction of real scenes 3D reconstruction is a process of obtaining data from real scenes and generating corresponding 3D models. There are many methods for 3D reconstruction of real scenes, which can be selected according to application requirements, available sensors and computing resources. The following are common methods for 3D reconstruction of real scenes:

Stereo vision: two or more cameras shoot images of the same scene, and the depth information of the midpoint of the scene is calculated through the parallax information between the images, thus creating a three-dimensional model.

Lidar: Use laser beam to measure the distance on the surface of an object, scan the laser beam and measure the return time to obtain point cloud data. These point cloud data can be used to generate a three-dimensional model of coordinates.

Structured light: Projecting light with a known mode with a projector, and capturing the deformation of the surface of the irradiated object through coordinates, so as to calculate the depth information of the surface.

Photogrammetry: Using a large number of camera images, by matching the feature points in the images, the depth information of the camera position and the midpoint of the scene can be calculated. This information can be used to generate three-dimensional models.

Deep learning: Using deep training learning technology, especially nonlinear neural network (CNN) and neural network structure, 3D information can be directly extracted from images or point cloud data through models.

Time-of-Flight Imaging: The time of flight of light is measured by camera or laser to obtain the depth information of each point in the scene.

Stereo scanning: Using mechanical or optical scanning system, point cloud data is obtained by scanning the surface of the object, and then a three-dimensional model is reconstructed.

These methods can be used alone or in combination to improve the accuracy and effect of reconstruction. Choosing the right method usually depends on the specific requirements of the application, the characteristics of the scene and the available technologies and resources.