3D Model Generation via Sparse Multi-View Triplane Reconstruction
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Solution Overview
Problem
Conventional 3D model generation systems face challenges in accurately reconstructing objects from sparse input images with disparate viewpoints, often requiring dense image captures and precise camera pose data, which increases data acquisition costs and processing times, and can lead to suboptimal results due to the lack of strong shape priors.
Innovation Solution
A system and method that uses a 2D-to-3D transformer to generate 3D models from sparse input images by encoding view encodings and 3D position embeddings, allowing for the generation of triplane features and pose information, enabling accurate reconstruction without explicit camera pose data and reducing the need for dense image captures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense image captures and precise camera pose data are used, then 3D reconstruction accuracy is improved, but data acquisition costs and processing times increase
Solution Approach 1:
The system performs preliminary encoding of view directions and 3D position embeddings into the transformer model during training, so that the model learns to infer 3D structure from sparse views without requiring real-time camera pose data processing. This pre-learning approach reduces processing time during actual 3D reconstruction operations.
Solution Approach 2:
The patent introduces triplane features as an intermediary representation that bridges 2D sparse input images and 3D reconstruction output. The triplane features encode geometric and appearance information in a compact form, allowing accurate 3D reconstruction from limited views without requiring dense image captures or explicit camera pose data.
2Measurement precision
If dense image captures are used, then 3D reconstruction accuracy is improved, but data acquisition costs increase
Solution Approach 1:
The system uses only a partial set of views (sparse inputs) rather than requiring complete dense coverage of the object from all angles. The transformer model with view encodings and 3D position embeddings is designed to infer missing information from limited observations, achieving acceptable reconstruction accuracy with fewer input images.
Solution Approach 2:
The triplane features serve as an intermediary that compactly represents 3D geometry and appearance, allowing the system to reconstruct 3D models from sparse inputs without needing to acquire large quantities of dense view images. This intermediate representation reduces the data acquisition burden significantly.
3Quantity of substance
If sparse input images are used, then data acquisition costs are reduced, but 3D reconstruction accuracy deteriorates
Solution Approach 1:
The patent transforms the problem from 2D image space to 3D feature space by introducing triplane features and 3D position embeddings. This dimensional transformation allows the model to leverage spatial relationships across multiple views and infer 3D structure even from sparse 2D inputs, maintaining accuracy while reducing data requirements.
Solution Approach 2:
The triplane features act as an intermediary representation that enriches sparse input information with learned 3D geometric priors. By encoding view directions and 3D positions into the transformer, the system can reconstruct accurate 3D models from limited views through this intermediate feature representation.
4Measurement precision
If explicit camera pose data are required, then 3D reconstruction accuracy is improved, but system complexity increases
Solution Approach 1:
The transformer model with view encodings and 3D position embeddings performs self-service by automatically inferring spatial relationships and camera poses from the input images themselves. The model learns to encode view directions and 3D positions internally, eliminating the need for external camera pose data or complex pose estimation modules.
Solution Approach 2:
The triplane features serve as an intermediary that internally represents camera pose and spatial information without requiring explicit external input. This intermediate representation allows the model to handle sparse inputs with unknown poses while maintaining reconstruction accuracy, reducing system complexity by integrating pose reasoning into the feature representation.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for 3D model generation include obtaining a plurality of input images depicting an object and a set of 3D position embeddings, where each of the plurality of input images depicts the object from a different perspective, encoding the plurality of input images to obtain a plurality of 2D features corresponding to the plurality of input images, respectively, generating 3D features based on the plurality of 2D features and the set of 3D position embeddings, and generating a 3D model of the object based on the 3D features.