Open-Set 3D object detection in LiDAR data as an Out-of-Distribution problem

• URL: http://arxiv.org/abs/2410.23767v1
• Date: Thu, 31 Oct 2024 09:29:55 GMT
• Title: Open-Set 3D object detection in LiDAR data as an Out-of-Distribution problem
• Authors: Louis Soum-Fontez, Jean-Emmanuel Deschaud, François Goulette,
• Abstract summary: 3D Object Detection from LiDAR data has achieved industry-ready performance in controlled environments. Our work redefines the open-set 3D Object Detection problem in LiDAR data as an Out-Of-Distribution (OOD) problem to detect outlier objects.
• Score: 6.131026007721572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Object Detection from LiDAR data has achieved industry-ready performance in controlled environments through advanced deep learning methods. However, these neural network models are limited by a finite set of inlier object categories. Our work redefines the open-set 3D Object Detection problem in LiDAR data as an Out-Of-Distribution (OOD) problem to detect outlier objects. This approach brings additional information in comparison with traditional object detection. We establish a comparative benchmark and show that two-stage OOD methods, notably autolabelling, show promising results for 3D OOD Object Detection. Our contributions include setting a rigorous evaluation protocol by examining the evaluation of hyperparameters and evaluating strategies for generating additional data to train an OOD-aware 3D object detector. This comprehensive analysis is essential for developing robust 3D object detection systems that can perform reliably in diverse and unpredictable real-world scenarios.

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