Pdf Single View 3d Object Reconstruction From Shape Priors In Memory

By switzerlandersing On Sep 12, 2025

Meta3D: Single-View 3D Object Reconstruction From Shape Priors In Memory | DeepAI

Meta3D: Single-View 3D Object Reconstruction From Shape Priors In Memory | DeepAI View a pdf of the paper titled single view 3d object reconstruction from shape priors in memory, by shuo yang and 4 other authors. Humans routinely use incomplete or noisy visual cues from an image to retrieve similar 3d shapes from their memory and reconstruct the 3d shape of an object.

(PDF) Single-View 3D Object Reconstruction From Shape Priors In Memory

(PDF) Single-View 3D Object Reconstruction From Shape Priors In Memory In this paper, we make an attempt to incorporate learning techniques into the 3d reconstruction problem. instead of proposing specific reconstruction rules from heuristics, we learn the prior shape knowledge from existing 3d models. Experimental results demonstrate that mem3d significantly improves reconstruction quality and performs favorably against state of the art methods on the shapenet and pix3d datasets. We propose a memory based framework for single view 3d object reconstruction, named mem3d. it in novatively retrieves similar 3d shapes from the con structed shape priors, and shows a powerful ability to reconstruct the 3d shape of objects that are heavily oc cluded or in a complex environment. In this paper, we propose shapehd, pushing the limit of single view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. the learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth.

Figure 3 From Single-View 3D Object Reconstruction From Shape Priors In Memory | Semantic Scholar

Figure 3 From Single-View 3D Object Reconstruction From Shape Priors In Memory | Semantic Scholar We propose a memory based framework for single view 3d object reconstruction, named mem3d. it in novatively retrieves similar 3d shapes from the con structed shape priors, and shows a powerful ability to reconstruct the 3d shape of objects that are heavily oc cluded or in a complex environment. In this paper, we propose shapehd, pushing the limit of single view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. the learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Inspired by the recent success of semi supervised image classification tasks, we propose ssp3d, a semi supervised framework for 3d reconstruction. in particular, we introduce an attention guided prototype shape prior module for guiding realistic object reconstruction. Is proposed to sequentially encode shape priors related to the input image and predict a shape speci c re ner. experimental results demonstrate that our meta3d outperforms state of he art meth. Propose a novel framework for 3d object reconstruction, named rsp3d. compared to the existing methods for single view and mutil view 3d object reconstruction that directly learn to transform image features into 3d representations, rsp3d constructs shape priors that are helpful to complete the missing image features to recover the 3d. Humans routinely use incomplete or noisy visual cues from an image to retrieve similar 3d shapes from their memory and reconstruct the 3d shape of an object. inspired by this, we propose a novel method, named mem3d, that explicitly constructs shape priors to supplement the missing information in the image.

Table 1 From Single-View 3D Object Reconstruction From Shape Priors In Memory | Semantic Scholar

Table 1 From Single-View 3D Object Reconstruction From Shape Priors In Memory | Semantic Scholar Inspired by the recent success of semi supervised image classification tasks, we propose ssp3d, a semi supervised framework for 3d reconstruction. in particular, we introduce an attention guided prototype shape prior module for guiding realistic object reconstruction. Is proposed to sequentially encode shape priors related to the input image and predict a shape speci c re ner. experimental results demonstrate that our meta3d outperforms state of he art meth. Propose a novel framework for 3d object reconstruction, named rsp3d. compared to the existing methods for single view and mutil view 3d object reconstruction that directly learn to transform image features into 3d representations, rsp3d constructs shape priors that are helpful to complete the missing image features to recover the 3d. Humans routinely use incomplete or noisy visual cues from an image to retrieve similar 3d shapes from their memory and reconstruct the 3d shape of an object. inspired by this, we propose a novel method, named mem3d, that explicitly constructs shape priors to supplement the missing information in the image.