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Aptiv

Industry researchnorthamerica · us
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Research library11linked papers
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Selected work

Representative Papers

Texture-Shape Bias Balancing for Robust Synthetic-to-Real Semantic Segmentation in Automotive NIR Imagery

Jun 12, 2026

This work addresses the significant performance degradation of semantic segmentation models trained on synthetic near-infrared (NIR) images when deployed in real-world automotive scenarios, primarily caused by domain shift and scarce annotations. To mitigate this, the authors propose a generative augmentation framework comprising Target Style Adaptation (TSA) and Voronoi Style Diversification (VSD). TSA leverages low-rank fine-tuned diffusion models with structure-preserving multi-signal control to translate synthetic images into realistic NIR styles, while VSD employs geometry-guided disentanglement to decouple texture and shape inductive biases. As the first systematic study on synthetic-to-real domain adaptation for automotive NIR imagery, the proposed method reduces domain gaps by 63.6% and 28.4% in internal and external scenes, respectively, substantially enhancing the robustness and generalization of diverse segmentation models in real-world conditions.

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AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery

May 23, 2026

This work addresses the significant performance degradation of conventional RGB-based object detection under extremely low-light conditions. To overcome this limitation, the authors propose a dual-stream fusion framework that integrates CLAHE-enhanced RGB images with voxelized event data. Central to their approach is an adaptive cross-modal attention mechanism grounded in minimum-variance linear estimation theory, which asymptotically approximates the Gauss–Markov optimal fusion weights. The study further establishes, for the first time, theoretical bounds relating the conservation properties of event voxelization to its temporal resolution. Evaluated on the LLE-VOS benchmark, the method achieves 65.54% recall, 53.85% precision, and 59.12% F1-score, substantially outperforming single-modality approaches and demonstrating both its illumination-adaptive capability and theoretical soundness.

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InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset

Apr 04, 2026

This work addresses the challenge of inaccurate relative camera pose estimation in in-cabin fisheye imaging, which suffers from severe distortion and spatial constraints. The authors propose a single-pass Transformer-based architecture that leverages a frozen DINOv2 backbone for feature extraction and a ViT-Small decoder to model geometric correspondences between reference and target images, directly regressing metric-scale translation and rotation. Notably, the model is trained exclusively on synthetic data yet achieves strong domain generalization to real in-cabin scenes without requiring known camera intrinsics, yielding physically plausible poses. Evaluated on both the newly introduced In-Cabin-Pose benchmark and the 7-Scenes dataset, the method demonstrates high accuracy and real-time performance, with code and dataset publicly released to support safety-critical driver monitoring applications.

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Recent publications

Latest Papers

Texture-Shape Bias Balancing for Robust Synthetic-to-Real Semantic Segmentation in Automotive NIR Imagery

Jun 12, 2026

This work addresses the significant performance degradation of semantic segmentation models trained on synthetic near-infrared (NIR) images when deployed in real-world automotive scenarios, primarily caused by domain shift and scarce annotations. To mitigate this, the authors propose a generative augmentation framework comprising Target Style Adaptation (TSA) and Voronoi Style Diversification (VSD). TSA leverages low-rank fine-tuned diffusion models with structure-preserving multi-signal control to translate synthetic images into realistic NIR styles, while VSD employs geometry-guided disentanglement to decouple texture and shape inductive biases. As the first systematic study on synthetic-to-real domain adaptation for automotive NIR imagery, the proposed method reduces domain gaps by 63.6% and 28.4% in internal and external scenes, respectively, substantially enhancing the robustness and generalization of diverse segmentation models in real-world conditions.

0 citationsRead paper

AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery

May 23, 2026

This work addresses the significant performance degradation of conventional RGB-based object detection under extremely low-light conditions. To overcome this limitation, the authors propose a dual-stream fusion framework that integrates CLAHE-enhanced RGB images with voxelized event data. Central to their approach is an adaptive cross-modal attention mechanism grounded in minimum-variance linear estimation theory, which asymptotically approximates the Gauss–Markov optimal fusion weights. The study further establishes, for the first time, theoretical bounds relating the conservation properties of event voxelization to its temporal resolution. Evaluated on the LLE-VOS benchmark, the method achieves 65.54% recall, 53.85% precision, and 59.12% F1-score, substantially outperforming single-modality approaches and demonstrating both its illumination-adaptive capability and theoretical soundness.

0 citationsRead paper

InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset

Apr 04, 2026

This work addresses the challenge of inaccurate relative camera pose estimation in in-cabin fisheye imaging, which suffers from severe distortion and spatial constraints. The authors propose a single-pass Transformer-based architecture that leverages a frozen DINOv2 backbone for feature extraction and a ViT-Small decoder to model geometric correspondences between reference and target images, directly regressing metric-scale translation and rotation. Notably, the model is trained exclusively on synthetic data yet achieves strong domain generalization to real in-cabin scenes without requiring known camera intrinsics, yielding physically plausible poses. Evaluated on both the newly introduced In-Cabin-Pose benchmark and the 7-Scenes dataset, the method demonstrates high accuracy and real-time performance, with code and dataset publicly released to support safety-critical driver monitoring applications.

0 citationsRead paper