PEFT-DML: Parameter-Efficient Fine-Tuning Deep Metric Learning for Robust Multi-Modal 3D Object Detection in Autonomous Driving

• URL: http://arxiv.org/abs/2512.00060v1
• Date: Sun, 23 Nov 2025 03:07:14 GMT
• Title: PEFT-DML: Parameter-Efficient Fine-Tuning Deep Metric Learning for Robust Multi-Modal 3D Object Detection in Autonomous Driving
• Authors: Abdolazim Rezaei, Mehdi Sookhak,
• Abstract summary: PEFT-DML is a parameter-efficient deep metric learning framework for robust 3D object detection in autonomous driving.<n>By integrating Low-Rank Adaptation (LoRA) and adapter layers, PEFT-DML achieves significant training efficiency.<n>Experiments on benchmarks nuScenes demonstrate superior accuracy.
• Score: 0.979731979071071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces PEFT-DML, a parameter-efficient deep metric learning framework for robust multi-modal 3D object detection in autonomous driving. Unlike conventional models that assume fixed sensor availability, PEFT-DML maps diverse modalities (LiDAR, radar, camera, IMU, GNSS) into a shared latent space, enabling reliable detection even under sensor dropout or unseen modality class combinations. By integrating Low-Rank Adaptation (LoRA) and adapter layers, PEFT-DML achieves significant training efficiency while enhancing robustness to fast motion, weather variability, and domain shifts. Experiments on benchmarks nuScenes demonstrate superior accuracy.

Related papers

• DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking [50.038098341549095]
  State estimation is challenging for 3D object tracking with high maneuverability.<n>We propose a novel framework, DIMM, to effectively combine estimates from different motion models in each direction.<n>DIMM significantly improves the tracking accuracy of existing state estimation methods by 31.61%99.23%.
  arXiv  Detail & Related papers  (2025-05-18T10:12:41Z)
• OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering [16.047505930360202]
  We present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli filter.<n>We show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-17T09:24:26Z)
• Efficient Multimodal 3D Object Detector via Instance-Level Contrastive Distillation [17.634678949648208]
  We introduce a fast yet effective multimodal 3D object detector, incorporating our proposed Instance-level Contrastive Distillation (ICD) framework and Cross Linear Attention Fusion Module (CLFM)<n>Our 3D object detector outperforms state-of-the-art (SOTA) methods while achieving superior efficiency.
  arXiv  Detail & Related papers  (2025-03-17T08:26:11Z)
• Easy-Poly: A Easy Polyhedral Framework For 3D Multi-Object Tracking [23.40561503456164]
  We present Easy-Poly, a real-time, filter-based 3D MOT framework for multiple object categories.<n>Results show that Easy-Poly outperforms state-of-the-art methods such as Poly-MOT and Fast-Poly.<n>These findings highlight Easy-Poly's adaptability and robustness in diverse scenarios.
  arXiv  Detail & Related papers  (2025-02-25T04:01:25Z)
• Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble [15.173314907900842]
  Existing 3D object detection methods rely heavily on the LiDAR sensor. We propose MEFormer to address the LiDAR over-reliance problem. Our MEFormer achieves state-of-the-art performance of 73.9% NDS and 71.5% mAP in the nuScenes validation set.
  arXiv  Detail & Related papers  (2024-07-27T03:21:44Z)
• Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving [58.16024314532443]
  We introduce LaserMix++, a framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to assist data-efficient learning.<n>Results demonstrate that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations.<n>This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.
  arXiv  Detail & Related papers  (2024-05-08T17:59:53Z)
• Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving [69.20604395205248]
  We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. We introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector. We conduct extensive experiments on the KITTI, runtime, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD.
  arXiv  Detail & Related papers  (2024-03-10T10:36:32Z)
• ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking [26.976216624424385]
  3D multi-object tracking (MOT) is essential for an autonomous mobile agent to safely navigate a scene. We aim to develop a 3D MOT framework that fuses camera and LiDAR sensor information.
  arXiv  Detail & Related papers  (2023-10-04T02:17:59Z)
• UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation [113.35352122662752]
  We present an efficient multi-modal backbone for outdoor 3D perception named UniTR. UniTR processes a variety of modalities with unified modeling and shared parameters. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks.
  arXiv  Detail & Related papers  (2023-08-15T12:13:44Z)
• SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection [56.24700754048067]
  Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging. We propose a Simulated multi-modal Distillation (SimDistill) method by carefully crafting the model architecture and distillation strategy. Our SimDistill can learn better feature representations for 3D object detection while maintaining a cost-effective camera-only deployment.
  arXiv  Detail & Related papers  (2023-03-29T16:08:59Z)
• The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [62.1185839286255]
  Low-cost monocular 3D object detection plays a fundamental role in autonomous driving. We introduce a Dynamic Feature Reflecting Network, named DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set.
  arXiv  Detail & Related papers  (2021-12-28T07:31:18Z)

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.