3D Environment Reconstruction via Automatic Object Segmentation
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Solution Overview
Problem
Existing methods for generating virtual reconstructions of physical environments typically produce a single 3D representation and require manual interaction for segmentation and object identification, which is burdensome and often results in suboptimal outcomes.
Innovation Solution
An end-to-end system that automatically generates a digital representation of environments by segmenting and identifying individual objects using image and sensor data, such as RGB-D data, without the need for motion capture or intensive user input, allowing for real-time feedback and accurate object classification and replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing scanning approaches are used to generate virtual reconstructions, then a single three-dimensional representation of the environment can be obtained, but the environment cannot be segmented into individual regions or objects and manual interaction is required
Solution Approach 1:
The patent applies segmentation by dividing the environment representation into distinct object instances. The system segments the three-dimensional environment into multiple independent object representations, where each object is identified and separated from the overall scene. This allows automatic identification of individual objects without manual interaction, resolving the contradiction between automation and complexity by using computational segmentation algorithms.
Solution Approach 2:
The patent uses an intermediary approach by introducing a processing system that acts as a mediator between the raw scan data and the final segmented object representations. This intermediary system automatically performs classification, segmentation, and object identification tasks, reducing the need for manual interaction while managing system complexity through modular architecture.
2Ease of operation
If manual interaction is used for segmenting and identifying objects, then object representations can be obtained, but the process is burdensome and complicated for users
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform segmentation and object identification without requiring user intervention. The processing system autonomously analyzes scan data, identifies objects, segments the environment, and generates object representations independently. This eliminates the burdensome manual interaction and significantly reduces the time required for these tasks.
Solution Approach 2:
The patent applies preliminary action by pre-processing the scan data to automatically classify and identify objects before any user interaction is needed. The system performs preliminary segmentation and object recognition tasks automatically, so when users do interact with the system, the heavy lifting of segmentation and identification has already been completed, reducing both effort and time requirements.
3Measurement precision
If existing approaches are used for object segmentation, then some object representations can be obtained, but the results are suboptimal and require manual refinement
Solution Approach 1:
The patent applies feedback by implementing iterative refinement processes where the system generates initial object segmentations, evaluates their accuracy, and automatically adjusts its segmentation algorithms to improve precision. The feedback mechanism allows the system to learn from its own outputs and continuously improve object identification accuracy without manual intervention, simultaneously improving precision and maintaining productivity.
Solution Approach 2:
The patent replaces manual mechanical segmentation processes with automated computational algorithms. Instead of relying on manual refinement and adjustment, the system uses machine learning and image processing algorithms to automatically achieve high-precision object identification and segmentation. This substitution eliminates the need for manual refinement while maintaining or improving accuracy, thereby increasing overall productivity.
Data Source
AI summary
Approaches presented herein can provide for the automatic generation of a digital representation of an environment that may include multiple objects of various object types. An initial representation (e.g., a point cloud) of the environment can be generated from registered image or scan data, for example, and objects in the environment can be segmented and identified based at least on that initial representation. For objects that are recognized based on these segmentations, stored accurate representations can be substituted for those objects in the representation of the environment, and if no such model is available then a mesh or other representation of that object can be generated and positioned in the environment. A result can then include a 3D representation of a scene or environment in which objects are identified and segmented as individual objects, and representations of the scene or environment can be viewed, and interacted with, through various viewports, positions, and perspectives.