Bidirectional RNN for Moving Point Detection in LiDAR Point Clouds
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Solution Overview
Problem
Traditional methods for detecting moving points in point clouds from lidar sensors on moving vehicles are complex and inefficient, often requiring multi-stage systems and struggling with the challenge of distinguishing moving objects from static ones, especially when dealing with dense three-dimensional data.
Innovation Solution
The implementation of a recurrent neural network (RNN) using stacked Gated Recurrent Units (GRUs) and bidirectional architecture to process voxelized representations of point clouds, combining forward and backward passes to predict whether points are moving or static, and updating the point cloud accordingly, while optimizing computational resources through 2D operations and gradient checkpointing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multi-stage systems are used for moving point detection, then detection capability is achieved, but system complexity and computational inefficiency increase
Solution Approach 1:
The patent combines multiple detection stages into a unified deep learning model that processes point cloud data end-to-end. The network integrates feature extraction, moving point classification, and object detection into a single trainable system, eliminating the need for separate multi-stage processing while maintaining detection reliability.
Solution Approach 2:
The patent replaces traditional mechanical multi-stage detection systems with a data-driven deep learning approach. Instead of sequential processing stages with hand-crafted features, the system uses neural networks to automatically learn optimal feature representations and detection strategies from data.
2Loss of information
If traditional methods process dense three-dimensional point cloud data, then complete spatial information is captured, but computational resources and processing time increase
Solution Approach 1:
The patent segments the dense point cloud data into meaningful groups or clusters before processing. By organizing points into semantic groups (e.g., by object instance or spatial region), the network can process information more efficiently while preserving the complete spatial relationships needed for accurate detection.
Solution Approach 2:
The patent transforms the three-dimensional point cloud data into alternative representations such as voxel grids or point embeddings that maintain spatial information while being more computationally efficient to process. This dimensionality transformation enables faster processing without losing critical spatial relationships.
3Productivity
If voxelized representations are used for point cloud processing, then computational efficiency is improved, but memory usage and data resolution may be reduced
Solution Approach 1:
The patent dynamically adjusts voxelization parameters such as resolution and grid size based on the specific application requirements and available computational resources. This allows the system to optimize the balance between computational efficiency and data resolution, using finer voxelization where high precision is needed and coarser voxelization in less critical regions.
Data Source
AI summary
Systems and methods for moving point detection for point clouds. Some implementations may include obtaining a sequence of point clouds, wherein the sequence of point clouds includes a current point cloud, a previous point cloud that precedes the current point cloud, and a next point cloud that follows the current point cloud; inputting a voxelized representation of the current point cloud to a forward-pass recurrent neural network with a hidden state based on the previous point cloud to obtain a first voxelized activation; inputting the voxelized representation of the current point cloud to a backward-pass recurrent neural network with a hidden state based on the next point cloud to obtain a second voxelized activation; combining the first voxelized activation and the second voxelized activation to obtain a prediction of whether respective points are moving or static; and updating the current point cloud based on the prediction.