Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation

Kernekoncepter

The author presents a novel framework for robust 3D object detection from LiDAR and radar point clouds using cross-modal hallucination, achieving superior performance in both modalities.

Resumé

This paper introduces a novel approach to enhance 3D object detection by leveraging cross-modal feature augmentation. By aligning spatial and feature levels between LiDAR and radar data, the proposed method outperforms state-of-the-art techniques on the View-of-Delft dataset. The framework addresses the challenges of sparse radar data and complements LiDAR's depth measurements with semantic information from radar, resulting in improved detection accuracy. The selective matching module ensures correct cross-modal instance matches, enhancing detection robustness even with single-modal input during inference. Extensive experiments demonstrate the effectiveness of the proposed method in achieving competitive efficiency while maintaining high accuracy.

Statistik

Our method achieves 41.77 mAP for radar object detection.
For LiDAR object detection, our method reaches an mAP of 69.62.
Memory usage for our method is 133MB.
Our method has an inference speed of 183 frames per second.

Citater

"Our proposed approach is agnostic to either hallucination direction between LiDAR and 4D radar."
"The trained object detection models can deal with difficult detection cases better."
"Our design enhances intra-modality features through cross-modal learning."

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Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation

by Jianning Den... kl. arxiv.org 03-12-2024

https://arxiv.org/pdf/2309.17336.pdf

Dybere Forespørgsler

How can the proposed framework be adapted for other applications beyond autonomous driving

The proposed framework for robust 3D object detection from LiDAR-Radar point clouds via cross-modal feature augmentation can be adapted for various applications beyond autonomous driving. One potential application is in robotics, where the fusion of LiDAR and radar data can enhance object detection capabilities in robotic navigation and manipulation tasks. For example, robots equipped with LiDAR and radar sensors can benefit from improved perception of their surroundings, leading to more efficient obstacle avoidance and path planning algorithms. Additionally, this framework could be applied in industrial automation settings to enhance safety measures by detecting objects or hazards in complex environments.

What are potential limitations or biases introduced by relying on hallucination techniques in object detection

While hallucination techniques offer a way to bridge the gap between different sensor modalities and improve object detection performance, they come with potential limitations and biases. One limitation is the reliance on synthetic data generation during hallucination, which may introduce inaccuracies or unrealistic features that could impact model generalization to real-world scenarios. Biases may arise from the inherent differences between sensor modalities that are not fully accounted for during hallucination, leading to skewed representations or misinterpretations of certain objects or scenes. Moreover, over-reliance on hallucinated features without proper validation against ground truth data could result in misleading detections or false positives.

How might advancements in sensor technology impact the future development of cross-modal learning frameworks

Advancements in sensor technology are poised to significantly impact the future development of cross-modal learning frameworks for object detection. Improved sensor resolution, range accuracy, and multi-modality integration capabilities will enable more precise data capture across different environmental conditions. This enhanced sensor technology will provide richer semantic information that can be leveraged by cross-modal frameworks for better feature alignment and representation learning. Furthermore, advancements such as higher frame rates and increased sensitivity will lead to more detailed point cloud data inputs for training models effectively.
These technological developments will drive innovation in cross-modal learning approaches by enabling finer-grained distinctions between objects based on diverse sensing modalities' unique characteristics like Radar Cross Section (RCS) measurements or Doppler velocity information.