Open-Vocabulary 3D Object Detection for Novel Class Localization
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Solution Overview
Problem
Existing 3D scene processing models struggle to effectively detect objects belonging to novel classes beyond a limited close vocabulary due to the scarcity of annotated training data, leading to inaccurate detection and localization of unseen classes.
Innovation Solution
A method involving a detecting model that associates base classes with 3D data and leverages a text-embedded semantic classifier and binary calibration module to recognize and localize novel classes, using image captions as a bridge for language supervision, enabling open vocabulary detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a detecting model is trained with limited annotated training data covering only a close vocabulary, then the model can effectively detect objects within that limited vocabulary, but it cannot effectively detect objects belonging to novel classes beyond the close vocabulary
Solution Approach 1:
The patent introduces image captions as an intermediary bridge between 2D image data and 3D scene understanding. The text-embedded semantic classifier uses these captions to provide language supervision, enabling the model to learn semantic relationships for novel classes without requiring annotated 3D training data for those classes. This intermediary mechanism allows the model to generalize to open vocabulary detection while maintaining reliability through the grounding provided by base class annotations.
2Adaptability or versatility
If annotated training data is expanded to cover more object classes, then the model's detection capability for novel classes improves, but the cost and complexity of data annotation increases significantly
Solution Approach 1:
The patent enables the detecting model to self-improve its capability for novel class detection by leveraging existing base class annotations and image captions. The binary calibration module automatically learns to distinguish between base and novel classes without requiring manual annotation of novel classes. The system serves itself by using the available annotated data more effectively through language supervision and semantic reasoning, eliminating the need for expensive and complex expansion of annotated training data.
3Measurement precision
If the model uses only base classes from annotated training data, then detection accuracy for those classes is high, but the model cannot recognize or localize novel classes that are not in the training vocabulary
Solution Approach 1:
The patent performs preliminary calibration by training a binary classifier to distinguish between base classes and novel classes before final detection. This preliminary action prepares the model to handle both known and unknown classes effectively. The text-embedded semantic classifier is also pre-trained with language supervision from image captions, enabling it to reason about novel classes even before encountering them during inference, thus maintaining accuracy for base classes while gaining adaptability for novel classes.
Data Source
AI summary
A method is proposed for detecting an object in a 3D scene, further including a detecting model is obtained; the detecting model describes an association relationship between a plurality of base classes of a plurality of objects and 3D data of the plurality of objects. A plurality of open classes of a plurality of candidate objects that are detected in a 3D scene are received, wherein the plurality of open classes comprises the plurality of base classes and at least one novel class not comprised in the plurality of base classes. A 3D portion is detected in 3D data of the 3D scene based on the detecting model and the plurality of open classes, and the 3D portion here corresponds to a target candidate object in the plurality of candidate objects. With the proposed method, objects that belong to a novel class may be detected from the 3D data.