Single-Image 3D Object Training Without Real-World 3D Labels
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Solution Overview
Problem
Existing autonomous and semi-autonomous driving systems face inefficiencies in 3D object recognition due to image processing inefficiencies, reliance on separate machine learning models for different object categories, and slow surface extraction during training, hindering speed and real-world applicability.
Innovation Solution
A multi-stage machine learning model training process combining 2D and 3D supervised training with synthetic and real-world data, utilizing a chamfer loss for self-supervision without direct 3D labels, and employing a backbone network with task-specific heads for end-to-end inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional machine learning models are used for 3D object recognition, then object detection can be performed, but processing speed is slow and real-time performance is hindered
Solution Approach 1:
The training process is segmented into three distinct stages: (1) 2D and 3D supervised pre-training on synthetic data, (2) 2D supervised mixed-training with synthetic and real-world data, and (3) 3D self-supervised fine-tuning on real-world data. This segmentation allows the model to progressively adapt to different data types and training requirements, achieving fast real-time inference while maintaining accuracy.
2Measurement precision
If separate machine learning models are used for different object categories, then specialized recognition can be achieved, but system complexity increases
Solution Approach 1:
A single unified machine learning model is trained to recognize multiple object categories simultaneously. The model processes images and outputs predictions for various object types (vehicles, pedestrians, cyclists, obstacles) in a single inference pass, eliminating the need for separate specialized models while maintaining high recognition accuracy across different categories.
3Manufacturing precision
If 3D labeled real-world data is used for training, then accurate 3D object reconstruction can be achieved, but data collection and processing becomes time-consuming
Solution Approach 1:
The patent uses synthetic data as a copy or representation of real-world 3D objects for initial training. Synthetic images and 3D models are generated from virtual environments, providing accurate 3D labels without the time-consuming process of annotating real-world data. This synthetic data serves as a proxy that enables efficient training while maintaining 3D reconstruction accuracy.
Solution Approach 2:
The model performs 3D self-supervised learning in the third training stage, where it generates its own 3D representations and labels from real-world images without requiring external 3D annotations. The model uses chamfer loss to compute pseudo-ground-truth 3D labels from 2D images, enabling it to self-verify and refine its 3D reconstruction capabilities independently.
4Measurement precision
If surface extraction is performed during training, then 3D shape information can be obtained, but training speed decreases
Solution Approach 1:
Surface extraction and 3D shape computation are performed preliminarily during the pre-training stages using synthetic data, where the ground truth 3D shapes are already available. The model learns to represent objects in 3D space during this preliminary phase, and then during fine-tuning on real-world data, the model refines these representations without repeating the computationally intensive surface extraction process, thereby maintaining speed while improving accuracy.
Data Source
AI summary
Systems and methods are provided for implementing a multi-stage, ML model training process for autonomous or semi-autonomous driving. The multi-stage ML model training process comprises (1) 2D and 3D supervised losses during a synthetic data ML model training, (2) 2D supervised on real-world data, and (3) 3D self-supervised losses on real-world data. The improved ML training process may not rely on 3D object recognition with real-world 3D labeled data. Once the ML model is trained, in some examples, the trained ML model can implement an inference process to predict the 3D shape, size, and 6D pose of objects within a single image, operate at a category level, and eliminate the need for computer-aided design (CAD) models during inference.