3D Occupancy Network Training for Label-Free Generic Object Recognition
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Solution Overview
Problem
Current object recognition methods, particularly in autonomous driving, are limited by reliance on predefined classes and require expensive annotated 3D data, lacking the ability to recognize all objects in 360° and are not scalable or adaptable without extensive labeling.
Innovation Solution
A self-supervised method using a 3D occupancy network trained with differentiable volume rendering, enabling generic object recognition through a 3D voxel grid representation, allowing queryable features via natural language and eliminating the need for expensive 3D labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If voxel-based 3D representations are used for generic scene geometry, then object recognition capability is improved, but enormous amounts of expensive annotated 3D data are required
Solution Approach 1:
The patent uses 2D image data as a copy or proxy for 3D scene representation. Instead of directly annotating expensive 3D data, the system extracts features from readily available 2D images and transforms them into 3D voxel space, creating a cheaper surrogate training approach that maintains generic object recognition capability
Solution Approach 2:
The patent introduces an intermediary process involving feature extraction networks and occupancy transformers that bridge 2D image data and 3D voxel representations. This intermediary pipeline enables training with 2D data while achieving 3D scene understanding, eliminating the need for direct 3D annotation
2Productivity
If predefined class sets are used for training, then training efficiency is improved, but the system is bound to predefined classes and cannot recognize unknown objects
Solution Approach 1:
The patent creates a universal feature extraction system that learns generic visual features from 2D images without being constrained to specific predefined classes. The occupancy network and feature extraction networks are designed to be class-agnostic, enabling the system to recognize both known and unknown objects through generic scene geometry understanding
Solution Approach 2:
The patent changes the training paradigm from class-specific supervised learning to self-supervised learning based on geometric consistency and occupancy predictions. By changing the loss functions and training objectives to focus on 3D spatial reasoning rather than class classification, the system achieves both efficiency and openness to unknown objects
3Measurement precision
If 3D annotated data is used for training occupancy networks, then geometric accuracy is improved, but the cost and complexity of data preparation increases significantly
Solution Approach 1:
The patent creates a copy of the 3D scene geometry implicitly through 2D image projections and back-projections. By using multiple 2D views and enforcing geometric consistency through occupancy predictions, the system reconstructs accurate 3D geometry without requiring actual 3D annotations, significantly reducing data preparation complexity
Solution Approach 2:
The system performs self-supervised learning by automatically generating training signals from the input 2D images themselves. The occupancy network learns to predict 3D occupancy grids that are consistent across multiple 2D views, using the images' own geometric constraints as supervision without external 3D annotations
Data Source
AI summary
A method and a device for training an occupancy network for classification and/or object recognition of an object in a scene.