Deep Shape from a Low Number of Silhouettes

Abstract

Despite strong progress in the field of 3D reconstruction from multiple views, holes on objects, transparency of objects and textureless scenes, continue to be open challenges. On the other hand, silhouette based reconstruction techniques ease the dependency of 3d reconstruction on image pixels but need a large number of silhouettes to be available from multiple views. In this paper, a novel end to end pipeline is proposed to produce high quality reconstruction from a low number of silhouettes, the core of which is a deep shape reconstruction architecture. Evaluations on ShapeNet [1] show good quality of reconstruction compared with ground truth.

3D Reconstruction of Simple Objects from A Single View Silhouette Image

“…To demonstrate the proposed model's ability for class-specific reconstruction from a single-view silhouette image, experiments are conducted for two object categories including the car and the plane. We also conduct 3D reconstruction experiments for both the staked networks and the single network [30] Fig4, and demonstrate the improvement of the proposed stacked network both qualitatively and quantitatively. Furthermore, results are calculated in voxel IoU in order to make comparison with the state of art single-view reconstruction work.…”

“…Among many frameworks that are applied, the most relevant works are learned NRSfM model with defined consistency and smoothness criterion [14], 3D-R2N2 network with adding 3D convolutional LSTM inside [3], 3D-INN framework with usage of 2 pre-trained models and a projection layer [29] , volumetric reconstruction of objects with pre-trained projective transformation code [31] and depth prediction from CNN neural networks [25,15,9]. A single network is built for 3D reconstruction from multiple-view silhouette images with multiple-view information [30].…”

“…The top are the concise architecture(top left) of stacked hierarchical networks and the detailed architecture(top right). The bottom are the concise architecture(bottom left) of the single network [30] and the details architecture(bottom left), the replicate layer is not represented. the 3D grid space.…”

“…In order to measure the quality of the reconstruction shape quantitatively, we measure the voxel overlap between the reconstructed shape and the ground truth shape(voxel IoU). Also, to represent the accuracy improvement of our stacked networks compared with the single network [30], we evaluate rebuilt shapes produced from the two networks for comparison. As illustrated in Fig5, for the reconstruction experiments of cars and planes, we follow the Eq.…”

Image-based System for 3D Visualization of Flow in Hydrodynamic Tunnel

“…A 3D Shape from Silhouette [23,24,25] technique has been applied for qualitative 3D flow jets reconstruction. The silhouette, or occluding contour of a shape in an image contains some information about the 3D shape of the object.…”