3D Object Reconstruction from Incomplete Single-View Images
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Solution Overview
Problem
Conventional 3D representation generation from incomplete 2D images fails to accurately reconstruct objects with occluded or unseen parts, resulting in incomplete and globally incoherent 3D models due to the lack of consideration for missing pixel information and reliance on single-view surface normal detection.
Innovation Solution
A 3D convolutional neural network captures global ordinal relations in a canonical 3D volume space, generating volumetric features by encoding incomplete 2D images and using a 3D discriminator to create a complete and coherent 3D geometry, enhanced by multiview-consistent surface normal fusion and texture inpainting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional neural networks are used to learn image features at each pixel of a 2D image, then high-quality local details can be generated, but the 3D representation is incomplete when parts of the object are occluded or outside the camera view
Solution Approach 1:
The patent transitions from 2D image space to 3D volume space by introducing a canonical 3D volume that encompasses the entire object. This volumetric representation allows the model to infer and generate 3D features for occluded regions by leveraging spatial relationships in three dimensions, thereby recovering information lost in 2D projections while maintaining local detail quality through pixel-aligned feature learning.
2Productivity
If only visible regions in a 2D image are used for 3D reconstruction, then processing is simpler and faster, but the resulting 3D model lacks global coherence and completeness
Solution Approach 1:
The patent performs preliminary action by pre-defining a canonical 3D volume space and establishing pixel-to-volume correspondence before processing the image. This preprocessing step creates a structured framework that guides the neural network to populate the 3D volume systematically, ensuring global coherence is maintained throughout the reconstruction process while efficiently utilizing only the visible image regions.
3Loss of information
If a 3D volume is used to represent the object, then complete and coherent 3D representations can be generated from incomplete 2D images, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by learning pixel-aligned features that are specific to each pixel's corresponding 3D volume location. This localized feature learning approach allows the model to process and represent each region of the 3D volume independently with appropriate detail, reducing overall computational complexity by avoiding the need to process the entire 3D volume uniformly while still achieving complete and coherent 3D representations.
Data Source
AI summary
A modeling system accesses a two-dimensional (2D) input image displayed via a user interface, the 2D input image depicting, at a first view, a first object. At least one region of the first object is not represented by pixel values of the 2D input image. The modeling system generates, by applying a 3D representation generation model to the 2D input image, a three-dimensional (3D) representation of the first object that depicts an entirety of the first object including the first region. The modeling system displays, via the user interface, the 3D representation, wherein the 3D representation is viewable via the user interface from a plurality of views including the first view.