3D Object Shape Completion Using NORF and Triplanar Diffusion
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Solution Overview
Problem
Existing systems face challenges in accurately estimating complete and robust 3D object geometry from limited observations without assuming known geometry or tight constraints, leading to uncertainty in object shape and pose, especially in real-world scenarios.
Innovation Solution
The system decouples shape completion into two multi-modal distributions using a normalized object reference frame (NORF) and triplanar processing, employing NORF and triplanar diffusion models to predict object shapes and poses from a single image, without relying on prior assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If systems use data from multiple cameras for inferring object geometries, then measurement precision of 3D object geometry is improved, but device complexity and hardware costs increase
Solution Approach 1:
The patent replaces the mechanical system of multiple physical cameras with a computational approach using a single camera and diffusion models. The system uses a single image input and processes it through a diffusion model that generates multiple hypothetical views and 3D geometry representations, substituting the need for multiple physical sensing devices with a computational model that synthesizes geometric information from limited observations.
Solution Approach 2:
The diffusion model creates synthetic copies of the object from a single image by generating multiple hypothetical views and 3D representations. The model learns to replicate what multiple cameras would capture by inferring and synthesizing geometric information, effectively copying the functionality of multiple cameras through computational generation rather than physical hardware.
2Adaptability or versatility
If systems complete objects within a 3D scene without assuming known geometry, then adaptability to unknown object categories is improved, but measurement precision and reliability of shape estimation deteriorate
Solution Approach 1:
The system changes the parameter space by using a diffusion model that operates in a probabilistic framework. Instead of relying on fixed geometric assumptions, the model samples from a distribution of possible shapes and geometries, allowing it to adapt to unknown object categories while maintaining estimation accuracy through probabilistic inference rather than deterministic constraints.
Solution Approach 2:
The diffusion model performs preliminary action by pre-learning the distribution of 3D shapes and geometries from training data. This pre-learning enables the system to handle unknown object categories during inference by sampling from the learned distribution, effectively preparing the system in advance to accommodate diverse and unfamiliar objects without requiring explicit geometric assumptions.
3Reliability
If systems use diffusion models to generate multiple hypotheses from joint pose and shape distribution, then reliability of shape completion is improved, but computational time and processing complexity increase
Solution Approach 1:
The patent segments the complex diffusion process into manageable stages: first generating multiple hypothetical views from the single input image, then processing these views through a 3D diffusion model to generate 3D geometry representations. This segmentation allows the system to break down the computationally intensive task into discrete, manageable steps, improving reliability while controlling processing time through staged computation.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to deriving a geometric projection of an object shape using a normalized object reference frame (NORF) information and completing the object shape from the geometric projection through diffusion and triplanar processing. In one embodiment, a method includes estimating a NORF image and a NORF normal for an object from an image and noise using a NORF diffusion model, the object having incomplete data. The method also includes deriving a projection of the object from a point cloud using the NORF image and the NORF normal. The method also includes predicting a completed shape for the object from the projection and triplanar noise using a triplanar diffusion model.