Endoscopic inspection is an important non-destructive testing method. Traditional 3D endoscopic reconstruction methods, such as polarization reconstruction and shading reconstruction, have the drawbacks of not being able to determine the actual size and positional information of the object. The stereo vision method is limited by its own operating principles and has the issue sparse reconstructed point clouds. These drawbacks greatly limit the applications of the endoscope. Therefore, this work proposes a joint dense 3D reconstruction method for endoscopic imaging of weak texture scenes. This method uses the shading reconstruction normal to correct the polarization reconstruction normal, then uses coordinate conversion and point cloud fusion to convert the polarization and shading 3D reconstruction results from the pixel coordinate system to the world coordinate system. It combined the reconstruction results from the polarization, shading and stereo vision in the world coordinate system, and the fusion coefficients are obtained by solving the minimum error model; then, a complete and detailed 3D reconstruction surface was obtained in the world coordinate system. This method could avoid the difficulty of obtaining real coordinates for 3D reconstruction of polarization and shading and the issue of the sparse point cloud afforded by stereo vision reconstruction for weak texture scenes. Finally, a dense point cloud corresponding to each pixel in the world coordinate system could be obtained. The combined dense 3D reconstruction method had an average error of <1% for length measurement of a 3D curve, which is of high significance for industrial endoscopic inspection.