Monocular 3D Object Localization Using Temporal Cost Volumes
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Solution Overview
Problem
Existing 3D object localization methods using monocular images are less accurate due to the ill-posed nature of distance estimation from a single image, and most methods fail to effectively utilize temporal information for improving localization accuracy.
Innovation Solution
A computer-implemented method that predicts 3D object localization by applying an optical flow model to consecutive monocular images to generate relative motion estimation and construct an object cost volume, using a Gated Recurrent Unit (GRU) for refinement, to predict 3D bounding boxes, object size, position, and yaw angle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If one monocular image is used for 3D object localization, then the method is simple and fast, but the localization accuracy is poor due to ill-posed distance estimation
Solution Approach 1:
The patent transitions from 2D monocular image analysis to 3D localization by constructing a 3D cost volume that aggregates temporal features across multiple time frames. This dimensional transformation enables accurate depth estimation by leveraging temporal information, resolving the ill-posed nature of monocular distance estimation while maintaining processing efficiency through the recurrent refinement loop.
2Measurement precision
If temporal information is utilized for 3D object localization, then localization accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent implements a recurrent refinement loop that continuously refines the 3D localization results across multiple time frames. The GRU-based refinement mechanism maintains temporal continuity by iteratively updating localization estimates, allowing the system to achieve high accuracy through progressive refinement rather than single-shot computation, thus managing computational complexity effectively.
Solution Approach 2:
The recurrent refinement loop incorporates feedback mechanisms where the refined 3D localization results from previous iterations are fed back into the cost volume construction process. This feedback enables the system to progressively improve localization accuracy by leveraging temporal correlations, resolving the contradiction between accuracy improvement and computational complexity through iterative optimization.
3Measurement precision
If object association and tracking steps are included, then temporal information can be utilized, but the processing pipeline becomes more complex
Solution Approach 1:
The patent merges the object association and tracking steps into a unified recurrent refinement framework. By integrating these functions into the GRU-based refinement loop, the system eliminates the need for separate association and tracking modules, thereby reducing processing pipeline complexity while still effectively utilizing temporal information for improved localization accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves improved 3D object localization accuracy by fully utilizing temporal information and motion cues, bypassing object association/tracking steps, and refining depth predictions through a recurrent refinement loop, resulting in more accurate 3D object size, position, and yaw angle estimation.
Implementation Method 1
predicting, by a joint object detection mechanism that applies an optical flow model to two consecutive input monocular images, pairs of two-dimensional (2D) bounding boxes
Implementation Method 2
updating, by a recurrent refinement loop of a Gated Recurrent Unit (GRU), the relative motion estimation based on the object cost volume and the object depth
Data Source
AI summary
A method provided for 3D object localization predicts pairs of 2D bounding boxes. Each pair corresponds to a detected object in each of the two consecutive input monocular images. The method generates, for each detected object, a relative motion estimation specifying a relative motion between the two images. The method constructs an object cost volume by aggregating temporal features from the two images using the pairs of 2D bounding boxes and the relative motion estimation to predict a range of object depth candidates and a confidence score for each object depth candidate and an object depth from the object depth candidates. The method updates the relative motion estimation based on the object cost volume and the object depth to provide a refined object motion and a refined object depth. The method reconstructs a 3D bounding box for each detected object based on the refined object motion and refined object depth.