Abstract
Object 3D reconstruction from a single-view image is an ill-posed problem. Inferring the self-occluded part of an object makes 3D reconstruction a challenging and ambiguous task. In this paper, we propose a novel neural network for generating a 3D-object point cloud model from a single-view image. The proposed network named 3D-ReConstnet, an end to end reconstruction network. The 3D-ReConstnet uses the residual network to extract the features of a 2D input image and gets a feature vector. To deal with the uncertainty of the self-occluded part of an object, the 3D-ReConstnet uses the Gaussian probability distribution learned from the feature vector to predict the point cloud. The 3D-ReConstnet can generate the determined 3D output for a 2D image with sufficient information, and 3D-ReConstnet can also generate semantically different 3D reconstructions for the self-occluded or ambiguous part of an object. We evaluated the proposed 3D-ReConstnet on ShapeNet and Pix3D dataset, and obtained satisfactory improved results.
Highlights
Reconstructing the shape of 3D objects from a single-view is the fundamental task of robot navigation and grasping, CAD, virtual reality and so on
Voxel representation suffers from two problems: sparse information and high computational complexity, especially in high resolution 3D object processing
The experimental results show that 3D-ReConstnet outperforms the state-of-art reconstruction methods in the task of single view 3D reconstruction
Summary
Reconstructing the shape of 3D objects from a single-view is the fundamental task of robot navigation and grasping, CAD, virtual reality and so on. Data-driven 3D object reconstruction has attracted more and more attention. There are two kinds of 3D object representations: voxel and point cloud. The voxel-based neural networks [1]–[3] can reconstruct 3D objects by generating voxelized three-dimensional occupancy grids. Voxel representation suffers from two problems: sparse information and high computational complexity, especially in high resolution 3D object processing. In order to make up for the deficiency of voxel expression, Fan et al [4] proposed point cloud-based 3D object reconstruction which is a deep learning method to study point cloud generation. The 3D point cloud of an object is composed of three-dimensional points uniformly sampled from the surface of the object. Point cloud model has scalability and flexibility, so we use point cloud as our 3D representation
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