Amodal Cuboids for Precise LiDAR Object Detection
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Solution Overview
Problem
Existing vehicle systems struggle to accurately represent the full extent and heading of detected objects using loose-fit cuboids, leading to inefficiencies in object detection and trajectory prediction.
Innovation Solution
The implementation of amodal cuboids, which are generated by defining loose-fit cuboids encompassing LiDAR data points, accumulating these points, and computing an extent to create a tightly fitting cuboid, allowing for precise object detection and trajectory prediction using machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If loose-fit cuboids are used to encompass LiDAR data points, then object detection coverage is improved, but manufacturing precision and measurement precision deteriorate
Solution Approach 1:
The method segments the cuboid generation process into multiple stages: first generating loose-fit cuboids to ensure coverage, then accumulating LiDAR points within these cuboids, and finally computing a tight-fit cuboid from the accumulated points. This segmentation allows each stage to optimize for its specific purpose without compromising overall performance.
Solution Approach 2:
The loose-fit cuboids serve as a preliminary action that ensures comprehensive coverage of LiDAR data points before the final tight-fit cuboid is computed. By pre-establishing coverage boundaries, the system ensures no points are missed while preparing for the precision-oriented final cuboid generation.
2Adaptability or versatility
If loose-fit cuboids are used to encompass LiDAR data points, then object detection coverage is improved, but manufacturing precision deteriorates
Solution Approach 1:
The cuboid generation process is segmented into distinct phases: initial loose-fit cuboid creation for coverage, point accumulation within these cuboids, and final tight-fit cuboid computation. This segmentation isolates the coverage function from the precision function, allowing each to optimize independently.
Solution Approach 2:
Loose-fit cuboids are generated as a preliminary step to ensure all LiDAR points are captured, even if the fit is not tight. This preliminary action prioritizes completeness over precision, which is then corrected in the subsequent tight-fit computation stage.
3Measurement precision
If amodal cuboids are generated through point accumulation and extent computation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses the LiDAR data points themselves to define the cuboid boundaries through accumulation and extent computation, rather than requiring external manual annotation or complex pre-trained models. The data serves its own purpose of defining the precise cuboid fit, reducing the need for additional complex processing systems.
Solution Approach 2:
The complex task of generating precise amodal cuboids is segmented into manageable operations: defining initial cuboids, accumulating points within them, computing extents from accumulated points, and generating final cuboids. This segmentation makes the overall complex process more tractable and implementable.
Data Source
AI summary
This document discloses system, method, and computer program product embodiments for detecting an object. For example, the method includes generating a plurality of cuboids by performing the following operations: defining a plurality of first cuboids each encompassing lidar data points that are plotted on a respective 3D graph of a plurality of 3D graphs; accumulating the lidar data points encompassed by the plurality of first cuboids; computing an extent using the accumulated lidar data points; and defining a second cuboid that has dimensions specified by the extent. The first cuboids and/or the second cuboid may be used to detect the object.