Hyeonjun Jeong

Juyeb Shin, Hyeonjun Jeong, Francois Rameau et al.

For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map construction plays a significant role in accurate estimation of the pose. Although recent advancements in online HD map construction have predominantly investigated on vectorized representation due to its effectiveness, they suffer from computational cost and fixed parametric model, which limit scalability. To alleviate these limitations, we propose a novel HD map learning framework that leverages graph modeling. This framework is designed to learn the construction of diverse geometric shapes, thereby enhancing the scalability of HD map construction. Our approach involves representing the map elements as an instance-level graph by decomposing them into vertices and edges to facilitate accurate and efficient end-to-end vectorized HD map learning. Furthermore, we introduce an association strategy using a Graph Neural Network to efficiently handle the complex geometry of various map elements, while maintaining scalability. Comprehensive experiments on public open dataset show that our proposed network outperforms state-of-the-art model by $1.6$ mAP. We further showcase the superior scalability of our approach compared to state-of-the-art methods, achieving a $4.8$ mAP improvement in long range configuration. Our code is available at https://github.com/juyebshin/InstaGraM.

Sanmin Kim, Youngseok Kim, Sihwan Hwang et al.

Recent advancements in camera-based 3D object detection have introduced cross-modal knowledge distillation to bridge the performance gap with LiDAR 3D detectors, leveraging the precise geometric information in LiDAR point clouds. However, existing cross-modal knowledge distillation methods tend to overlook the inherent imperfections of LiDAR, such as the ambiguity of measurements on distant or occluded objects, which should not be transferred to the image detector. To mitigate these imperfections in LiDAR teacher, we propose a novel method that leverages aleatoric uncertainty-free features from ground truth labels. In contrast to conventional label guidance approaches, we approximate the inverse function of the teacher's head to effectively embed label inputs into feature space. This approach provides additional accurate guidance alongside LiDAR teacher, thereby boosting the performance of the image detector. Additionally, we introduce feature partitioning, which effectively transfers knowledge from the teacher modality while preserving the distinctive features of the student, thereby maximizing the potential of both modalities. Experimental results demonstrate that our approach improves mAP and NDS by 5.1 points and 4.9 points compared to the baseline model, proving the effectiveness of our approach. The code is available at https://github.com/sanmin0312/LabelDistill

Chaehee Song, Sanmin Kim, Hyeonjun Jeong et al.

Vision-based 3D occupancy prediction has made significant advancements, but its reliance on cameras alone struggles in challenging environments. This limitation has driven the adoption of sensor fusion, among which camera-radar fusion stands out as a promising solution due to their complementary strengths. However, the sparsity and noise of the radar data limits its effectiveness, leading to suboptimal fusion performance. In this paper, we propose REOcc, a novel camera-radar fusion network designed to enrich radar feature representations for 3D occupancy prediction. Our approach introduces two main components, a Radar Densifier and a Radar Amplifier, which refine radar features by integrating spatial and contextual information, effectively enhancing spatial density and quality. Extensive experiments on the Occ3D-nuScenes benchmark demonstrate that REOcc achieves significant performance gains over the camera-only baseline model, particularly in dynamic object classes. These results underscore REOcc's capability to mitigate the sparsity and noise of the radar data. Consequently, radar complements camera data more effectively, unlocking the full potential of camera-radar fusion for robust and reliable 3D occupancy prediction.

Hyeonjun Jeong, Juyeb Shin, Dongsuk Kum

Neural radiance fields (NeRFs) have emerged as a prominent pre-training paradigm for vision-centric autonomous driving, which enhances 3D geometry and appearance understanding in a fully self-supervised manner. To apply NeRF-based pretraining to 3D perception models, recent approaches have simply applied NeRFs to volumetric features obtained from view transformation. However, coupling NeRFs with view transformation inherits conflicting priors; view transformation imposes discrete and rigid representations, whereas radiance fields assume continuous and adaptive functions. When these opposing assumptions are forced into a single pipeline, the misalignment surfaces as blurry and ambiguous 3D representations that ultimately limit 3D scene understanding. Moreover, the NeRF network for pre-training is discarded during downstream tasks, resulting in inefficient utilization of enhanced 3D representations through NeRF. In this paper, we propose a novel NeRF-Resembled Point-based 3D detector that can learn continuous 3D representation and thus avoid the misaligned priors from view transformation. NeRP3D preserves the pre-trained NeRF network regardless of the tasks, inheriting the principle of continuous 3D representation learning and leading to greater potentials for both scene reconstruction and detection tasks. Experiments on nuScenes dataset demonstrate that our proposed approach significantly improves previous state-of-the-art methods, outperforming not only pretext scene reconstruction tasks but also downstream detection tasks.