Neural Semantic Fields for 3D Segmentation From 2D Annotations
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Solution Overview
Problem
Existing methods for three-dimensional semantic segmentation require costly and complex three-dimensional annotated reference data, making it challenging to produce accurate three-dimensional semantically segmented scene representations.
Innovation Solution
A machine-learned semantic segmentation model framework that processes two-dimensional images to generate three-dimensional semantic classifications using neural radiance field techniques, leveraging implicit geometry representations and supervised learning from two-dimensional semantic annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional annotated reference data is used for semantic segmentation, then segmentation accuracy is improved, but data acquisition cost and complexity increase
Solution Approach 1:
The patent creates a three-dimensional scene representation that copies and reconstructs the geometric and semantic structure of the real scene from multiple two-dimensional images. This virtual 3D copy allows semantic segmentation to be performed in three dimensions without requiring costly 3D annotated data, as the segmentation labels can be derived from 2D image annotations through the learned mapping
Solution Approach 2:
The patent introduces a machine-learned model as an intermediary that bridges 2D image data and 3D semantic segmentation. This model learns to map from 2D image coordinates and features to 3D scene representations and semantic labels, enabling accurate 3D segmentation without direct 3D annotation
2Manufacturing precision
If three-dimensional annotated reference data is collected, then segmentation quality is improved, but time and resource consumption increase
Solution Approach 1:
The patent performs preliminary action by training the machine-learned model on 2D annotated images beforehand. Once trained, the model can rapidly perform 3D semantic segmentation without requiring time-consuming 3D data annotation for each new scene. The preliminary training on 2D data establishes the mapping capabilities that enable fast 3D segmentation inference
Solution Approach 2:
Instead of creating 3D annotated data copies which are time-consuming, the patent copies 2D annotations through the learned 2D-to-3D mapping model. This allows segmentation quality to be maintained while dramatically reducing the time required for data preparation and annotation
3Measurement precision
If complex three-dimensional data processing is performed, then spatial understanding accuracy is improved, but computational cost increases
Solution Approach 1:
The patent performs dimensionality change by mapping problems from 3D space to 2D image space where computations are more efficient. The model processes queries in 3D but leverages 2D image projections and corresponding 2D annotations for training and inference, reducing computational complexity while maintaining spatial understanding accuracy
Data Source
AI summary
Example embodiments of the present disclosure provide an example computer-implemented method for constructing a three-dimensional semantic segmentation of a scene from two-dimensional inputs. The example method includes obtaining, by a computing system comprising one or more processors, an image set comprising one or more views of a subject scene. The example method includes generating, by the computing system and based at least in part on the image set, a scene representation describing the subject scene in three dimensions. The example method includes generating, by the computing system and using a machine-learned semantic segmentation model framework, a multidimensional field of probability distributions over semantic categories, the multidimensional field defined over the three dimensions of the subject scene. The example method includes outputting, by the computing system, classification data for at least one location in the subject scene.