Voxel-Based 3D Object Reconstruction from Single 2D Image Using Variational Autoencoders

In recent years, learning-based approaches for 3D reconstruction have gained much popularity due to their encouraging results. However, unlike 2D images, 3D cannot be represented in its canonical form to make it computationally lean and memory-efficient. Moreover, the generation of a 3D model directly from a single 2D image is even more challenging due to the limited details available from the image for 3D reconstruction. Existing learning-based techniques still lack the desired resolution, efficiency, and smoothness of the 3D models required for many practical applications. In this paper, we propose voxel-based 3D object reconstruction (V3DOR) from a single 2D image for better accuracy, one using autoencoders (AE) and another using variational autoencoders (VAE). The encoder part of both models is used to learn suitable compressed latent representation from a single 2D image, and a decoder generates a corresponding 3D model. Our contribution is twofold. First, to the best of the authors’ knowledge, it is the first time that variational autoencoders (VAE) have been employed for the 3D reconstruction problem. Second, the proposed models extract a discriminative set of features and generate a smoother and high-resolution 3D model. To evaluate the efficacy of the proposed method, experiments have been conducted on a benchmark ShapeNet data set. The results confirm that the proposed method outperforms state-of-the-art methods.

Image-based 3D reconstruction precision using a camera mounted on a robot arm

In this paper, we study 3D object reconstruction based on a set of 2D images. In order to get the best camera path that increases accuracy we focus on this strategy to be used. Euclidean 3D image-based reconstruction is developed in three steps, which are primitive extraction, correspondence of these primitives and then triangulation. The extraction and triangulation are purely geometrical, whereas the matching step can have precision issues especially in the case of noisy images. An experimental study is carried out where a camera is attached to a robot arm and moved precisely relative to a scene containing a checkerboard calibration pattern. The reconstruction results are compared with values of motion given to the robot. A geometric and analytical study of the impact of the motion of the camera with respect to the scene on the error of a 3D image-based reconstructed point was also carried out. It has been demonstrated that the impact of a correspondence error on the reconstruction accuracy point varies drastically depending on the image capture strategy.