3D Object Detection Fusion for Occluded and Distant Targets
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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 detection precision deteriorates due to limitations such as cameras struggling with fine-grained 3D information and LIDAR providing sparse observations
Solution Approach 1:
The patent combines multiple sensors (camera and LIDAR) into a unified detection system that processes both image data and point cloud data simultaneously. The multi-sensor fusion architecture merges the complementary strengths of each sensor type to achieve superior detection precision while managing system complexity through integrated processing pipelines
2Measurement precision
If multi-sensor fusion is implemented, then detection precision improves by combining LIDAR and image data, but device complexity increases due to multiple sensors and processing requirements
Solution Approach 1:
The patent implements a multi-functional processing system where the same computational architecture handles multiple sensor types and multiple detection tasks (object detection, depth completion, feature extraction). This universal processing framework manages the complexity of multi-sensor fusion by using a unified approach rather than separate dedicated processing paths for each sensor
Solution Approach 2:
The patent transforms LIDAR point cloud data into a bird's eye view representation, changing the dimensional perspective from 3D point coordinates to a 2D top-down view. This dimensional transformation facilitates easier fusion with 2D image data and simplifies the processing complexity while maintaining detection precision
3Measurement precision
If joint end-to-end training is performed for multi-task learning, then detection precision improves through better feature representations, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary feature extraction and processing in the training pipeline, pre-computing feature representations from both LIDAR and image data before the main detection task. This preliminary action reduces the computational burden during end-to-end training and accelerates the overall training process while maintaining detection accuracy
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.