mlsanityData to 14 Oct 2026

Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge

arXiv 2309.09593 ↗First posted 18 Sep 2023cs.CV cs.IT cs.ROcited by 9

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Abstract (arXiv)

In the expanding landscape of AI-enabled robotics, robust quantification of predictive uncertainties is of great importance. Three-dimensional (3D) object detection, a critical robotics operation, has seen significant advancements; however, the majority of current works focus only on accuracy and ignore uncertainty quantification. Addressing this gap, our novel study integrates the principles of conformal inference (CI) with information theoretic measures to perform lightweight, Monte Carlo-free uncertainty estimation within a multimodal framework. Through a multivariate Gaussian product of the latent variables in a Variational Autoencoder (VAE), features from RGB camera and LiDAR sensor data are fused to improve the prediction accuracy. Normalized mutual information (NMI) is leveraged as a modulator for calibrating uncertainty bounds derived from CI based on a weighted loss function. Our simulation results show an inverse correlation between inherent predictive uncertainty and NMI throughout the model's training. The framework demonstrates comparable or better performance in KITTI 3D object detection benchmarks to similar methods that are not uncertainty-aware, making it suitable for real-time edge robotics.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 31 of its 43 references that are in the map. First posted on arXiv 18 Sep 2023.

Builds on

31 of 43 references.

References · 31

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19 more
  1. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 326
  2. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  3. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  4. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
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  8. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  9. 2019LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Drivingcited by 133
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  11. 2021MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagationcited by 82
  12. 2019Learning an Uncertainty-Aware Object Detector for Autonomous Drivingcited by 34
  13. 2019Learning Representations by Maximizing Mutual Information in Variational Autoencoderscited by 20
  14. 2018Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detectioncited by 19
  15. 2020It's All Around You: Range-Guided Cylindrical Network for 3D Object Detectioncited by 13
  16. 2023Lightweight, Uncertainty-Aware Conformalized Visual Odometrycited by 13
  17. 2020Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Losscited by 10
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  19. 2023Uncertainty-Aware AB3DMOT by Variational 3D Object Detectioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 2.

9 papers.

  1. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 14
  2. 2025EigenTrack: Spectral Activation Feature Tracking for Hallucination and Out-of-Distribution Detection in LLMs and VLMscited by 10
  3. 2025Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perceptioncited by 7
  4. 2025Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perceptioncited by 6
  5. 2025INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomycited by 5
  6. 2025Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challengescited by 5
  7. 2026Uncertainty-Guided Inference-Time Depth Adaptation for Transformer-Based Visual Trackingcited by 3
  8. 2024Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approachcited by 1
  9. 2026Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibrationcited by 0