Multi View 3d Object Reconstruction And Uncertainty Modelling With Neural Shape Prior Deepai

Multi-view 3D Object Reconstruction And Uncertainty Modelling With Neural Shape Prior | DeepAI

Multi-view 3D Object Reconstruction And Uncertainty Modelling With Neural Shape Prior | DeepAI We evaluate the system in both synthetic and real datasets to demonstrate the effectiveness of uncertainty based fusion to improve 3d object reconstruction accuracy. Abstract 3d object reconstruction is important for semantic scene understanding. it is challenging to reconstruct detailed 3d shapes from monocular images directly due to a lack of depth information, occlusion and noise. most current methods generate deterministic object models without any awareness of the uncertainty of the reconstruction.

A Hybrid Mesh-neural Representation For 3D Transparent Object Reconstruction | DeepAI

A Hybrid Mesh-neural Representation For 3D Transparent Object Reconstruction | DeepAI We propose a 3d object modeling approach that relies on an implicit neural representation and provides both 3d object reconstruction and an uncertainty measure for each object. We evaluate the system in both synthetic and real datasets to demonstrate the effectiveness of uncertainty based fusion to improve 3d object reconstruction accuracy. This work derives a probabilistic formulation that propagates shape and pose uncertainty through two novel loss functions and explicitly model the uncertainty of the object shapes and poses during the authors' optimization, resulting in a high quality object level mapping system. We propose a 3d object modeling approach that relies on neural implicit representation and provides both an object reconstruction and an uncertainty measure for each object.

NeurAR: Neural Uncertainty For Autonomous 3D Reconstruction | DeepAI

NeurAR: Neural Uncertainty For Autonomous 3D Reconstruction | DeepAI This work derives a probabilistic formulation that propagates shape and pose uncertainty through two novel loss functions and explicitly model the uncertainty of the object shapes and poses during the authors' optimization, resulting in a high quality object level mapping system. We propose a 3d object modeling approach that relies on neural implicit representation and provides both an object reconstruction and an uncertainty measure for each object. We propose a 3d object modelling approach that relies on an implicit neural representation and provides both a 3d object reconstruction and an uncertainty measure for each object. Deep learning based 3d reconstruction neural networks have achieved good performance on generating 3d features from 2d features. however, they often lead to feature loss in reconstruction. in this paper we propose a multi view object 3d reconstruction neural network, named p2vnet. In figure s1, we show a simple ex ample of propagating the uncertainty in the 3d shape model into any given camera view with differentiable rendering. we sample multiple latent codes and calculate the sample mean and variance of the rendered depth of each latent code. We tackle this problem by leveraging a neural object representation which learns an object shape distribution from large dataset of 3d object models and maps it into a latent space.

Toward Realistic Single-View 3D Object Reconstruction w. Unsupervised Learning from Multiple Images