Single-Image 3D Model Generation Using Transformer and NeRF
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Solution Overview
Problem
Conventional image processing techniques for generating 3D models from images require multiple coherent images and metadata, limiting their applicability and generalization to new domains without significant retraining.
Innovation Solution
A transformer-based autoencoder architecture processes a single input image to generate 3D shape data using a neural radiance field (NeRF) representation, inferring camera pose and encoding 2D features to 3D features through self- and cross-attention mechanisms, enabling domain-agnostic 3D model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple coherent images and metadata are used for generating 3D models, then the accuracy and reliability of 3D reconstruction is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent segments the complex multi-image processing task into a single-image processing pipeline using a transformer-based autoencoder. The encoder processes a single input image to extract features, while the decoder generates multiple views and 3D representations independently, eliminating the need for coordinated multi-image capture systems.
Solution Approach 2:
The patent introduces a transformer-based autoencoder as an intermediary that bridges the gap between single 2D images and 3D representations. This intermediary model learns to infer depth and spatial relationships from single images, replacing the need for multiple coherent images as intermediaries in the reconstruction process.
2Ease of manufacture
If conventional image processing techniques are used for 3D model generation, then the manufacturing process is well-established, but the adaptability to new domains and object categories is limited
Solution Approach 1:
The patent employs a transformer-based autoencoder with learnable parameters that adapt to different object categories and domains. The model's parameters are trained on diverse datasets, enabling it to generalize across different object types without requiring domain-specific processing pipelines or manual configuration changes.
Solution Approach 2:
The patent creates a universal 3D generation system that can process various object categories and domains using a single transformer-based autoencoder model. The model performs multiple functions including feature extraction, 3D representation generation, and multi-view synthesis, replacing the need for domain-specific processing techniques.
3Adaptability or versatility
If prior embeddings and retraining are used for new domains, then the model can adapt to specific domains, but the time and computational resources required increase
Solution Approach 1:
The patent implements a self-service adaptation mechanism where the transformer-based autoencoder automatically adapts to new domains through its inherent generalization capabilities. The model processes images from new domains using its pre-trained parameters without requiring external retraining interventions, domain-specific embeddings, or computational resource-intensive fine-tuning processes.
Data Source
AI summary
Systems and methods for generating a 3D model from a single input image are described. Embodiments are configured to obtain an input image and camera view information corresponding to the input image; encode the input image to obtain 2D features comprising a plurality of 2D tokens corresponding to patches of the input image; decode the 2D features based on the camera view information to obtain 3D features comprising a plurality of 3D tokens corresponding to regions of a 3D representation; and generate a 3D model of the input image based on the 3D features.