3D Object Detection Fusion for Occluded and Distant Objects
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Solution Overview
Problem
Current 3D object detection systems for autonomous vehicles face challenges in accurately detecting occluded or distant objects due to the limitations of individual sensors, such as cameras struggling with fine-grained 3D information and LIDAR providing sparse observations.
Innovation Solution
A multi-sensor fusion approach that jointly trains machine-learned models to perform multi-task learning, combining LIDAR and image data for improved feature representation, including point-wise and ROI-wise fusion, and depth completion to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual sensors (camera or LIDAR) are used for 3D object detection, then device complexity is reduced, but measurement precision and detection accuracy deteriorate due to limitations in capturing fine-grained 3D information or sparse observations
Solution Approach 1:
The patent combines multiple sensor types (LIDAR and camera) into a unified detection system that processes both point cloud data and image data simultaneously. The LIDAR backbone model processes 3D point cloud information while the image backbone model processes 2D visual information, and their features are fused to achieve accurate 3D object detection, thereby resolving the contradiction between using simple individual sensors and achieving high detection precision.
Solution Approach 2:
The detection system is designed to perform multiple functions: it processes both LIDAR point cloud data and camera image data, performs feature extraction from multiple modalities, and generates comprehensive 3D object detections. This multi-functional architecture allows the system to overcome the limitations of individual sensors while maintaining a unified processing framework.
2Reliability
If multi-sensor fusion is implemented, then detection accuracy for occluded or distant objects improves, but computational complexity and training difficulty increase
Solution Approach 1:
The patent divides the complex multi-sensor processing task into separate specialized modules: a LIDAR backbone model for processing point cloud data, an image backbone model for processing camera images, and a refinement model for fusing features and generating final detections. This segmentation allows each module to be optimized independently while working together to achieve robust detection in challenging conditions.
Solution Approach 2:
The refinement model acts as an intermediary that receives feature representations from both the LIDAR backbone model and the image backbone model, fuses these features, and generates the final object detections. This intermediary structure manages the complexity of multi-sensor fusion by providing a dedicated interface for integrating information from multiple sources.
3Measurement precision
If joint end-to-end training of multiple models is performed, then feature representation quality and detection performance improve, but training time and computational resources increase
Solution Approach 1:
The patent implements continuous end-to-end training of the multi-sensor detection system, where gradients from the loss function propagate through the refinement model and both backbone models simultaneously. This continuous joint training ensures that all components learn optimized feature representations together, improving detection performance while the parallel processing architecture minimizes training time penalties.
Data Source
AI summary
Provided are systems and methods that perform multi-task and/or multi-sensor fusion for three-dimensional object detection in furtherance of, for example, autonomous vehicle perception and control. In particular, according to one aspect of the present disclosure, example systems and methods described herein exploit simultaneous training of a machine-learned model ensemble relative to multiple related tasks to learn to perform more accurate multi-sensor 3D object detection. For example, the present disclosure provides an end-to-end learnable architecture with multiple machine-learned models that interoperate to reason about 2D and/or 3D object detection as well as one or more auxiliary tasks. According to another aspect of the present disclosure, example systems and methods described herein can perform multi-sensor fusion (e.g., fusing features derived from image data, light detection and ranging (LIDAR) data, and/or other sensor modalities) at both the point-wise and region of interest (ROI)-wise level, resulting in fully fused feature representations.