Occluded Object Detection via Generative Shadow Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reconstructing the 3D shape of occluded objects is challenging due to under-constrained problems and occlusions, which hinder the detection of their shape and pose from a single image.
Innovation Solution
A method using a generative model that iteratively optimizes a latent vector to generate a predicted object and its shadow, comparing it with an observed shadow to refine the object's shape and pose, and modifying the latent vector until a loss threshold is met, allowing for the detection of occluded objects without requiring additional views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If reconstruction is performed from a single image, then the processing time is reduced, but the measurement precision deteriorates due to under-constrained problems and occlusions
Solution Approach 1:
The patent introduces shadows as an intermediary element to bridge the gap between single-image input and accurate 3D reconstruction. By capturing and analyzing shadows cast by occluded objects, the system obtains additional geometric constraints that would normally require multiple camera views, thereby maintaining measurement precision while processing only a single image
Solution Approach 2:
The patent replaces the mechanical/optical system of multiple cameras with a computational approach using deep learning models. Instead of using multiple physical sensors to capture different views, the system uses a single image with shadow information processed through neural networks to achieve 3D reconstruction, substituting physical complexity with computational intelligence
2Measurement precision
If multiple camera views are used to improve reconstruction accuracy, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
Shadows serve as an intermediary that provides the missing geometric information normally obtained from multiple views. The shadow contains depth and spatial relationship data that allows accurate 3D reconstruction from a single viewpoint, eliminating the need for complex multi-camera setups while maintaining measurement precision
Solution Approach 2:
The patent creates a computational copy of the multi-view reconstruction process through deep learning. The neural network is trained to learn the mapping from single images with shadows to 3D object representations, effectively copying the information-gathering capability of multiple cameras into a software-based solution that uses only a single sensor
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively reconstructs the 3D shape and pose of occluded objects by utilizing shadows as constraints, enabling accurate detection and manipulation in robotic and autonomous systems.
Implementation Method 1
An observed shadow of the occluded object is obtained. The shadow provides critical geometric information about the occluded object's shape, size, and position, enabling reconstruction despite occlusion
Data Source
AI summary
Systems and methods for detecting occluded objects are disclosed. In one embodiment, a method of determining a shape and pose of an object occluded by an occlusion object includes receiving, by a generative model, a latent vector, and iteratively performing an optimization routine until a loss is less than a loss threshold. The optimization routine includes generating, by the generative model, a predicted object having a shape and a pose from the latent vector, generating a predicted shadow cast by the predicted object, calculating the loss by comparing the predicted shadow with an observed shadow, and modifying the latent vector when the loss is greater than the loss threshold. The method further includes selecting the predicted object as the object when the loss is less than the loss threshold.