Monocular 3D Vehicle Modeling via Semantic Keypoints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for 3D object detection in autonomous vehicles rely heavily on labor-intensive and costly human annotation, particularly for 3D cuboids, and struggle with scaling up annotation pipelines, as they require large amounts of annotated datasets for supervised training, which is challenging and expensive.
Innovation Solution
The method employs 2D semantic keypoints for auto-labeling, using a differentiable shape renderer and a continuously traversable coordinate shape-space that combines signed-distance-field (SDF) shapes with normalized object coordinates (NOCS) to facilitate 3D object modeling and auto-labeling, reducing the need for extensive human annotation by leveraging synthetic and real data in a learning curriculum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If labor-intensive human annotation is used for 3D object detection, then annotation accuracy can be maintained, but the time consumption and cost increase significantly
Solution Approach 1:
The patent uses 2D semantic keypoints as simplified copies or representations of 3D object structures. Instead of manually annotating complex 3D bounding boxes, the system detects 2D keypoint positions in images and uses these as proxies to automatically generate 3D annotations, dramatically reducing annotation time while maintaining acceptable accuracy
Solution Approach 2:
The system enables self-service annotation by automatically generating 3D object annotations from 2D keypoint detections without requiring human annotators. The automated pipeline processes images and generates 3D bounding box annotations independently, eliminating the need for manual 3D annotation labor
2Measurement precision
If extensive human annotation is used for supervised training, then model training accuracy improves, but the cost and scalability deteriorate
Solution Approach 1:
The patent creates a scalable annotation system by copying 2D keypoint detection results to generate synthetic 3D annotations. This approach allows large volumes of training data to be automatically produced without proportional increases in human annotation resources, enabling scalable model training
Solution Approach 2:
The system provides self-service annotation capabilities that automatically generate training data for supervised learning. The automated annotation pipeline serves the model training process without external human intervention, enabling unlimited scalability of training datasets
3Loss of information
If 3D object annotation is performed manually, then annotation detail and completeness improve, but the manpower requirements and costs increase
Solution Approach 1:
The patent uses 2D semantic keypoints as lightweight copies that capture essential object structure information. These 2D representations contain sufficient detail for generating accurate 3D annotations without requiring manual creation of full 3D models, reducing manpower while preserving annotation quality
Solution Approach 2:
The system extracts only the essential 2D keypoint information needed for 3D annotation generation, rather than requiring complete manual 3D annotation. This extraction approach maintains annotation detail by focusing on critical geometric features while eliminating unnecessary manual labor
Data Source
AI summary
A method for 3D object modeling includes linking 2D semantic keypoints of an object within a video stream into a 2D structured object geometry. The method includes inputting, to a neural network, the object to generate a 2D NOCS image and a shape vector, the shape vector being mapped to a continuously traversable coordinate shape. The method includes applying a differentiable shape renderer to the SDF shape and the 2D NOCS image to render a shape of the object corresponding to a 3D object model in the continuously traversable coordinate shape space. The method includes lifting the linked, 2D semantic keypoints of the 2D structured object geometry to a 3D structured object geometry. The method includes geometrically and projectively aligning the 3D object model, the 3D structured object geometry, and the rendered shape to form a rendered object. The method includes generating 3D bounding boxes from the rendered object.