A Convolutional Neural Network for Nonrigid Structure from Motion

Abstract

In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network. Compared with the traditional method for directly solving 3D structures, our model focuses on depth information that is lost owing to projection. This mathematical model is developed using a convolutional neural network with three modules for integration, reconstruction, and optimization, as well as two prior-free loss functions. The proposed RONN achieves competitive accuracy on several tested sequences and high visual quality of various real video sequences.

Deep Spatial-Temporal Neural Network for Dense Non-Rigid Structure from Motion

“…Comparing to actual images, our method successfully reconstructed the 3D structure of the actor mocap. Furthermore, we qualitatively compared our approach to face model learning (FML) [50], scalable monocular surface reconstruction (SMSR) [51], consolidating monocular dynamic reconstruction (CMDR) [52,53], optimization neural network (RONN) [54], and N-NRSfM [9]. Moreover, e 3D for the actor mocap is listed in Table 3, which revealed that our method is significantly superior to other methods.…”

“…The widely usage of synthetic faces allows us to compare DST-NRSfM to more methods. To more intuitively understand the efficacy of our approach, we compared the experimental outcomes of DST-NRSfM with classical sparse NRSfM methods, such as metric projections (MP) [55], complementary rank-3 spaces (CSF2) [17], block-matrix-method (BMM) [20], and organic priors based approach (OP) [3], traditional dense NRSfM methods, such as variational approach (VA) [7], dense spatio-temporal approach (DSTA) [25], CMDR [52,53], Grassmannian manifold (GM) [8], jumping manifolds (JM) [29], SMSR [51], and probabilistic point trajectory approach (PPTA) [14], and the latest neural-based dense NRSfM approaches, such as N-NRSfM [9], and RONN [54]. Table 4 presents the final comparative experimental results, where OP is the newest method that solves the dense NRSfM problem by extending the sparse approach to the dense domain; however, the accuracy of our proposed framework remained nearly twice as accuracy.…”

Temporal-Aware Neural Network for Dense Non-Rigid Structure from Motion

“…In the evaluation of the Synthetic Face dataset, a comparison was conducted between the 3D reconstruction framework employed and classical traditional methods such as VA [8] and CMDR [39]. Additionally, an assessment was made of newer traditional methods, including GM [9], JM [19], SMSR [21], PPTA [40], and EM-FEM [7], along with neural network methods like N-NRSfM [6], RONN [41], NTP [38], and DST-NRSFM [42]. Furthermore, comparisons were carried out with other relevant methods.…”

“…VA [8] CMDR [39] PPTA [40] JM [19] SMSR [21] GM [9] Traj Actor Mocap: The Actor Motion Capture dataset comprises 100 frames, encompassing a total of 36,349 points. To gauge the effectiveness of the method on this dataset, a comparative evaluation was conducted against FML [43], SMSR [21], CMDR [39], RONN [41], N-NRSFM [6], and DST-NRSFM [42]. The outcomes, as shown in Table 2, reveal the superior performance and accuracy achieved by the proposed method.…”

Face Recognition Model Design Based on Convolutional Neural Networks

“…e main principal of convolutional neural network is the face feature extraction and the training of neural network model, so the structure of convolutional neural network will determine the effect of face recognition behind [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. e convolutional neural network system has designed eight layers of neural network, including three convolutional layers, three pooling layers, one fully connected layer and one output layer.…”