Object Shell Mesh Reconstruction From Single Depth Images
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Solution Overview
Problem
Current 3D reconstruction methods from a single camera viewpoint are limited by the need for multiple images from different viewpoints, noise in observed images, and lack of generalizability to real-world environments, particularly in robotic grasp planning where true scale models and pose registration are crucial.
Innovation Solution
The method employs an object shell representation using depth images that capture entry and exit points of camera rays, allowing for direct representation in the camera coordinate frame, and utilizes a prediction network with skip connections in UNet architecture to generate accurate 3D object meshes from a single depth image, effectively handling noise and varying camera poses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images from different camera viewpoints are used for 3D reconstruction, then reconstruction accuracy is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent segments the 3D reconstruction problem into two parts: using a single camera to capture depth information, and using a neural network to synthesize the missing viewpoint information. This divides the complex multi-camera system into a simple capture device plus a computational model, reducing hardware complexity while maintaining reconstruction accuracy.
Solution Approach 2:
The patent introduces a neural network as an intermediary that translates a single-view depth image into a complete 3D object mesh. This intermediary compensates for the limited information from a single viewpoint, effectively bridging the gap between single-camera input and accurate 3D reconstruction output without requiring multiple physical cameras.
2Measurement precision
If pose registration is performed to align reconstruction with robot coordinate frame, then true scale model is achieved, but computational cost and potential errors increase
Solution Approach 1:
The patent performs pose registration and scale calibration in advance during the training phase of the neural network. The network learns the transformation from camera coordinate frame to object coordinate frame beforehand, so that during actual use, the pre-registered mesh can be directly applied without real-time computational overhead, saving time while maintaining accuracy.
Solution Approach 2:
The patent creates a pre-registered copy of the object mesh in the target coordinate frame during training. This copied and pre-transformed mesh can be directly used for robotic grasp planning without requiring real-time pose registration, eliminating computational delays while preserving scale accuracy.
3Quantity of substance
If synthetic datasets are used for training reconstruction methods, then training data volume is increased, but generalizability to real-world environments decreases
Solution Approach 1:
The patent merges synthetic training data with real-world depth images during the training process. By combining these two data sources, the network learns both from the large volume of synthetic data and the realistic characteristics of real-world images, achieving both sufficient training data volume and good generalizability to real environments.
Solution Approach 2:
The training dataset is constructed as a composite of synthetic rendered images and real depth images. This composite approach allows the network to benefit from the quantity and control of synthetic data while incorporating the realism and variability of real-world data, resulting in a model that generalizes well to real environments.
4Measurement precision
If noise removal is applied to depth images, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent incorporates noise removal into the training process of the neural network, where the network learns to denoise depth images during training. During actual use, the pre-trained network automatically performs noise removal as part of its inference process, eliminating the need for separate noise filtering steps and reducing overall processing time while maintaining depth image quality.
Data Source
AI summary
A depth image is used to obtain a three dimensional (3D) geometry of an object as an object mesh. The object mesh is obtained using an object shell representation. The object shell representation is based on a series of depth images denoting the entry and exit points on the object surface that camera rays would pass through. Given a set of entry points in the form of a masked depth image of an object, an object shell (an entry image and an exit image) is generated. Since entry and exit images contain neighborhood information given by pixel adjacency, the entry and exit images provide partial meshes of the object which are stitched together in linear time using the contours of the entry and exit images. A complete object mesh is provided in the camera coordinate frame.