Single-shot 3D shape reconstruction using deep convolutional neural networks
A robust single-shot 3D shape reconstruction technique integrating the fringe projection profilometry (FPP) technique with the deep convolutional neural networks (CNNs) is proposed in this letter. The input of the proposed technique is a single FPP image, and the training and validation data sets are prepared by using the conventional multi-frequency FPP technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D images to its corresponding 3D shape. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Shape ReconstructionSimilar Papers 제목 키워드 기반
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. However, recent work …
3D ReconstructionDecoderFew-Shot LearningMemorization+2Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. Recent work has chall…
3D ReconstructionDecoderFew-Shot LearningSingle-View 3D ReconstructionImage-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image
Shape reconstruction of deformable organs from two-dimensional X-ray images is a key technology for image-guided intervention. In this paper, we propose an image-to-graph convolutional network (IGCN) for deformable shape…
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization
We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set which will be fed into another learning a…
3D Shape Reconstruction3D Shape RepresentationFew-Shot LearningGeneral Classification+1Single-Shot Shape and Reflectance with Spatial Polarization Multiplexing
We propose spatial polarization multiplexing (SPM) for reconstructing object shape and reflectance from a single polarimetric image and demonstrate its application to dynamic surface recovery. Although single-pattern str…
BRDF estimation