In the field of autonomous driving, simulators have gained growing attention as they provide a cost-effective environment to collect data with various sensor modalities across diverse driving scenarios. Yet, the domain gap between real and synthetic data remains a significant barrier. To address this, numerous methods have been proposed to enhance the usability of synthetic data by bridging the gap. However, many of those approaches often focus on a single sensor setup, either a monocular camera or a LiDAR, potentially leading to inconsistent domain adaptation across sensors. As recent perception models actively employ sensor-fusion approaches like camera+LiDAR, we propose Sensor-wise Offline Dataset Adaptation (SODA), designed for a multimodal synthetic driving dataset with multi-view cameras and a LiDAR. It ensures consistency between sensor modalities after domain adaptation by enhancing signal information (camera pixel and LiDAR intensity values) while preserving their shape (image contents, point cloud geometry). To validate our method, MORDA (synthetic) and nuScenes (real) are opted as source and target datasets. In our extensive evaluation, incorporating SODA-applied MORDA during training of BEVFusion (camera+LiDAR) achieves mAP gain of 1.5% compared to training the same model solely on nuScenes. Further, ~2% improvement in mAP is observed under challenging adversecondition scenarios.

Learning implicit templates as neural fields has recently shown impressive performance in unsupervised shape correspondence. Despite the success, we observe current approaches, which solely rely on geometric information, often learn suboptimal deformation across generic object shapes, which have high structural variability. In this paper, we highlight the importance of part deformation consistency and propose a semantic-aware implicit template learning framework to enable semantically plausible deformation. By leveraging semantic prior from a self-supervised feature extractor, we suggest local conditioning with novel semantic-aware deformation code and deformation consistency regularizations regarding part deformation, global deformation, and global scaling. Our extensive experiments demonstrate the superiority of the proposed method over baselines in various tasks: keypoint transfer, part label transfer, and texture transfer. More interestingly, our framework shows a larger performance gain under more challenging settings. We also provide qualitative analyses to validate the effectiveness of semantic-aware deformation. The code is available at https://github.com/mlvlab/PDC.