MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving

• URL: http://arxiv.org/abs/2605.10026v1
• Date: Mon, 11 May 2026 05:50:08 GMT
• Title: MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving
• Abstract summary: We propose a novel framework for multi-source, multi-modality unsupervised domain adaptation in 3D object detection for autonomous driving.<n>Our framework effectively integrates information across both modalities and source domains, consistently outperforming state-of-the-art methods.
• Score: 6.084235400921738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advancement of autonomous driving, numerous annotated multi-modality datasets have become available. This presents an opportunity to develop domain-adaptive 3D object detectors for new environments without relying on labor-intensive manual annotations. However, traditional domain adaptation methods typically focus on a single source domain or a single modality, limiting their effectiveness in multi-source, multi-modality scenarios. In this paper, we propose a novel framework for multi-source, multi-modality unsupervised domain adaptation in 3D object detection for autonomous driving. Given multiple labeled source domains and one unlabeled target domain, our framework first introduces hierarchical spatially-conditioned (HSC) domain classifiers, which jointly align features from both camera and LiDAR modalities at two distinct levels for each source-target domain pair. To effectively leverage information from multiple source domains, we construct a prototype graph between each pair of domains. Based on this, we develop a prototype graph weighted (PGW) multi-source fusion strategy to aggregate predictions from multiple source detection heads. Experimental results on three widely used 3D object detection datasets - Waymo, nuScenes, and Lyft - demonstrate that our proposed framework effectively integrates information across both modalities and source domains, consistently outperforming state-of-the-art methods.

Related papers

• From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning [13.282416396765392]
  We introduce the first generalized cross-domain few-shot (GCFS) task in 3D object detection.<n>Our solution integrates multi-modal fusion and contrastive-enhanced prototype learning within one framework.<n>To effectively capture domain-specific representations for each class from limited target data, we propose a contrastive-enhanced prototype learning.
  arXiv  Detail & Related papers  (2025-03-08T17:05:21Z)
• Multimodal 3D Object Detection on Unseen Domains [37.142470149311904]
  Domain adaptation approaches assume access to unannotated samples from the test distribution to address this problem. We propose CLIX$text3D$, a multimodal fusion and supervised contrastive learning framework for 3D object detection. We show that CLIX$text3D$ yields state-of-the-art domain generalization performance under multiple dataset shifts.
  arXiv  Detail & Related papers  (2024-04-17T21:47:45Z)
• CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection [14.063365469339812]
  LiDAR-based 3D Object Detection methods often do not generalize well to target domains outside the source (or training) data distribution. We introduce a novel unsupervised domain adaptation (UDA) method, called CMDA, which leverages visual semantic cues from an image modality. We also introduce a self-training-based learning strategy, wherein a model is adversarially trained to generate domain-invariant features.
  arXiv  Detail & Related papers  (2024-03-06T14:12:38Z)
• Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer [69.82229895838577]
  Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to target a new and challenging setting of knowledge transfer that extends ADA from a single source domain to multiple source domains.
  arXiv  Detail & Related papers  (2023-11-21T13:12:21Z)
• Dynamic Instance Domain Adaptation [109.53575039217094]
  Most studies on unsupervised domain adaptation assume that each domain's training samples come with domain labels. We develop a dynamic neural network with adaptive convolutional kernels to generate instance-adaptive residuals to adapt domain-agnostic deep features to each individual instance. Our model, dubbed DIDA-Net, achieves state-of-the-art performance on several commonly used single-source and multi-source UDA datasets.
  arXiv  Detail & Related papers  (2022-03-09T20:05:54Z)
• Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency [90.71745178767203]
  Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. Existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world. We study a more realistic setting, unsupervised 3D domain adaptive detection, which only utilizes source domain annotations.
  arXiv  Detail & Related papers  (2021-07-23T17:19:23Z)
• Multi-Source Domain Adaptation for Object Detection [52.87890831055648]
  We propose a unified Faster R-CNN based framework, termed Divide-and-Merge Spindle Network (DMSN) DMSN can simultaneously enhance domain innative and preserve discriminative power. We develop a novel pseudo learning algorithm to approximate optimal parameters of pseudo target subset.
  arXiv  Detail & Related papers  (2021-06-30T03:17:20Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Mutual Learning Network for Multi-Source Domain Adaptation [73.25974539191553]
  We propose a novel multi-source domain adaptation method, Mutual Learning Network for Multiple Source Domain Adaptation (ML-MSDA) Under the framework of mutual learning, the proposed method pairs the target domain with each single source domain to train a conditional adversarial domain adaptation network as a branch network. The proposed method outperforms the comparison methods and achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-29T04:31:43Z)

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.