3D Object Modeling With Spatial Distribution and View Warping
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Solution Overview
Problem
Existing NeRF-based implementations face challenges such as the need for strict supervision using paired data during training, low inference speed, limited real-time applications, and difficulty in learning 3D semantic representations, which hinder efficient 3D modeling for metaverse applications.
Innovation Solution
A method utilizing a NeRF 360 framework that decouples coordinate and direction mapping by attaching a view-related 3D warping module, enabling efficient 3D scene modeling suitable for portable devices, and incorporating symmetric camera poses for supervised training to enhance 3D representation learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If NeRF-based implementations use deep convolutional neural networks to learn implicit representations, then 3D model processing capability is improved, but inference speed decreases and real-time applications are limited
Solution Approach 1:
The patent segments the 3D scene into discrete voxel grid representations, dividing the continuous implicit field into discrete volumetric elements. This segmentation enables faster querying and rendering by pre-computing and storing spatial occupancy information in a structured grid format, resolving the contradiction between processing capability and inference speed.
Solution Approach 2:
The patent performs preliminary actions by pre-training the neural network to learn spatial occupancy distributions and pre-computing voxel grid representations during the training phase. This preliminary learning of implicit representations allows the system to achieve real-time inference by simply querying pre-learned spatial distributions without heavy computation during rendering.
2Measurement precision
If strict supervision using paired data is used during training, then training accuracy is improved, but training complexity and data requirements increase
Solution Approach 1:
The patent makes the training framework universal by using unpaired images from a single viewpoint to train multiple functions simultaneously: spatial occupancy prediction, depth estimation, and novel view synthesis. This multi-functional approach eliminates the need for complex paired data while maintaining training accuracy, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
Instead of using the conventional approach of requiring paired images for supervised training, the patent inverts the approach by using unpaired single-view images and leveraging the learned spatial distributions to generate synthetic views for self-supervised training. This inversion eliminates data pairing requirements while maintaining training effectiveness.
3Measurement precision
If the NeRF framework processes 3D scenes using continuous implicit representations, then representation accuracy is improved, but computational efficiency decreases
Solution Approach 1:
The patent transitions from processing continuous 3D implicit representations to discrete voxel grid representations, adding a discrete dimensional structure to the continuous field. This dimensionality change enables efficient spatial indexing and querying operations while preserving the accuracy of implicit representations, resolving the contradiction between representation accuracy and computational efficiency.
Data Source
AI summary
A method for modeling an object in one embodiment includes: acquiring first position spatial information of a camera at a first position and second position spatial information of the camera at a second position, wherein the first position is different from the second position; generating a spatial distribution based on the first position spatial information and the second position spatial information, wherein the spatial distribution represents a probability distribution of space occupied by the object captured from a pose of the camera; generating a third image based on a first image captured at the first position, the spatial distribution, and the first position spatial information; generating a fourth image based on a second image captured at the second position, the spatial distribution, and the second position spatial information; and adjusting a model of the object based on the first image, the second image, the third image, and the fourth image.