3D Model Reconstruction from Point Clouds and Text
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Solution Overview
Problem
Existing point cloud completion techniques struggle with reconstructing complete 3D models from incomplete data due to limited viewpoints, occlusion, and low-resolution sampling, and perform poorly on Out-Of-Distribution objects not represented in training datasets.
Innovation Solution
A system using a pre-trained text-to-image diffusion model to reconstruct 3D objects from incomplete point clouds and textual descriptions, without requiring a training dataset of 3D objects, by initializing a 3D representation and iteratively updating parameters based on text-compatibility and sensor losses to produce a complete 3D model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing point cloud completion techniques use predefined training datasets, then reconstruction accuracy for known object types improves, but the system fails to generalize to Out-Of-Distribution objects
Solution Approach 1:
The system performs preliminary learning of object shape priors from 2D image data before encountering the actual 3D reconstruction task. The pre-trained diffusion model learns general object geometry and appearance from large-scale 2D image datasets, enabling it to handle unseen 3D objects without requiring pre-collected 3D training data.
Solution Approach 2:
The patent introduces 2D images as an intermediary medium between the training data and the 3D reconstruction task. Instead of directly training on 3D point clouds, the system uses 2D images as an intermediate representation to learn object priors, which are then transferred to 3D reconstruction through the diffusion model framework.
2Loss of information
If multiple viewpoints are captured to improve completeness, then more complete data is obtained, but the complexity and time required for data collection increases
Solution Approach 1:
The system applies partial action by using only a single viewpoint for data collection, rather than capturing multiple viewpoints. The diffusion model compensates for the incomplete information from a single viewpoint by leveraging learned object priors to infer missing geometry, thereby reducing data collection time while maintaining reconstruction quality.
Solution Approach 2:
The system enables self-service by allowing the diffusion model to automatically infer and complete missing geometric information from incomplete single-viewpoint data. The model serves itself by using its internal learned priors to fill in gaps without requiring additional external data collection efforts.
3Reliability
If traditional deep neural network methods are used with training datasets, then reconstruction works well for training distribution objects, but the system requires extensive training data collection and processing
Solution Approach 1:
The patent applies universality by using a single pre-trained diffusion model that can handle multiple object types and domains. The model learns universal object priors from diverse 2D image data, enabling it to generalize across different object categories without requiring separate training datasets for each object type.
Solution Approach 2:
The system substitutes the mechanical process of collecting and processing extensive 3D training data with a learning-based approach using 2D images. Instead of mechanically gathering 3D point clouds for training, the system uses computational learning from 2D images to infer 3D geometry, replacing data collection mechanics with intelligent inference.
Data Source
AI summary
Embodiments of the present disclosure relate to controlling generation of 3D objects using point clouds and text. Systems and methods are disclosed that leverage a pre-trained text-to-image diffusion model to reconstruct a complete 3D model of an object from a sensor-captured incomplete point cloud for the object and a textual description of the object. The complete 3D model of the object may be represented as a neural surface (signed distance function), polygonal mesh, radiance field (neural surface and volumetric coloring function), and the like. The signed distance function (SDF) measures the distance of any 3D point from the nearest surface point, where positive or negative signs indicate that the point is outside or inside the object respectively. The SDF enables use of the incomplete point cloud for constraining the surface location by simply encouraging the signed distance function to be zero in the point cloud locations.