Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

• URL: http://arxiv.org/abs/2002.00216v1
• Date: Sat, 1 Feb 2020 14:24:51 GMT
• Title: Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
• Authors: Di Feng, Yifan Cao, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
• Abstract summary: This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertainties in the classification and regression tasks, and leverage uncertainties to train the fusion network via a sampling mechanism.
• Score: 12.310862288230075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertainties in the classification and regression tasks, and leverage uncertainties to train the fusion network via a sampling mechanism. We validate our method on three datasets with challenging real-world driving scenarios. Experimental results show that the predicted uncertainties reflect complex environmental uncertainty like difficulties of a human expert to label objects. The results also show that our method consistently improves the Average Precision by up to 7% compared to the baseline method. When sensors are temporally misaligned, the sampling method improves the Average Precision by up to 20%, showing its high robustness against noisy sensor inputs.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge [1.7898305876314982]
  Three-dimensional (3D) object detection, a critical robotics operation, has seen significant advancements. Our study integrates the principles of conformal inference with information theoretic measures to perform lightweight, Monte Carlo-free uncertainty estimation. The framework demonstrates comparable or better performance in KITTI 3D object detection benchmarks to similar methods that are not uncertainty-aware.
  arXiv  Detail & Related papers  (2023-09-18T09:02:44Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling [7.096611243139798]
  Real-world driving datasets often suffer from the problem of data imbalance. We propose a method to address this data imbalance problem. Our experiment with KITTI and nuScenes datasets confirms our proposed method's effectiveness.
  arXiv  Detail & Related papers  (2022-10-07T05:23:25Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations [68.0524382279567]
  We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. Our approach outperforms supervised learning methods and achieves $93.8-98.1%$ accuracy.
  arXiv  Detail & Related papers  (2021-10-08T04:08:31Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty [12.531126969367774]
  We leverage our previously proposed method for estimating uncertainty inherent in ground truth bounding box parameters. Experimental results on the KITTI dataset show that our method surpasses both the baseline model and the models based on simple uncertaintys by up to 3.6% in terms of Average Precision.
  arXiv  Detail & Related papers  (2020-08-10T14:49:49Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.