Self-Supervised 3D Reconstruction via Semantic Consistency
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Solution Overview
Problem
Determining a 3D shape, texture, and camera pose from a 2D image of an object is challenging due to ambiguity in the 2D image, especially without additional information like multiple views or a 3D representation.
Innovation Solution
A self-supervised single-view 3D reconstruction model that predicts 3D mesh shape, texture, and camera pose using a neural network trained on 2D images and silhouettes, representing objects as deformable parts with semantic consistency, enabling reconstruction without pre-defined templates or 3D annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple views or 3D representation are used, then ambiguity in 2D image is reduced and 3D reconstruction is simplified, but additional information and complexity are required
Solution Approach 1:
The patent segments the 3D reconstruction problem into two distinct components: a category-level template mesh that captures common structural features, and instance-specific deformations that capture object variations. This segmentation allows the system to reconstruct 3D shapes from single 2D views by combining template information with learned deformations, avoiding the need for multiple views while maintaining reconstruction accuracy
Solution Approach 2:
The patent performs preliminary action by pre-defining category-specific template meshes that encode prior knowledge about object structures. These templates are constructed before reconstruction and serve as priors that guide the reconstruction process, enabling accurate 3D shape recovery from limited 2D input without requiring multiple views or complex iterative optimization
2Measurement precision
If category-specific templates are used, then reconstruction accuracy is improved, but adaptability to various object categories is reduced
Solution Approach 1:
The patent creates a universal template construction framework that can be applied to any object category. The system uses part segmentation networks and canonical UV maps that are category-agnostic in structure but adapt to specific categories through training. This allows the same template-based reconstruction approach to work across diverse object categories (birds, horses, cars, etc.) without requiring category-specific algorithms, achieving both accuracy and adaptability
3Reliability
If 3D annotations are required, then supervision quality is improved, but data collection complexity and cost increase
Solution Approach 1:
The patent implements self-service by enabling the system to generate its own supervision signals from unannotated 2D images. The template mesh and canonical UV maps serve as self-generated priors that provide structural constraints during reconstruction. The system uses consistency between rendered template features and detected image features as self-supervision, eliminating the need for expensive 3D annotations while maintaining reliable learning
Data Source
AI summary
Apparatuses, systems, and techniques to identify a shape or camera pose of a three-dimensional object from a two-dimensional image of the object. In at least one embodiment, objects are identified in an image using one or more neural networks that have been trained on objects of a similar category and a three-dimensional mesh template.