MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection (2209.03102v3)

Published 7 Sep 2022 in cs.CV

Abstract: Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. This is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space, and then incorporate 2D semantics via cross-modal interaction or fusion techniques. However, depth information is under-investigated in these approaches when lifting points into 3D space, thus 2D semantics can not be reliably fused with 3D points. Moreover, their multi-modal fusion strategy, which is implemented as concatenation or attention, either can not effectively fuse 2D and 3D information or is unable to perform fine-grained interactions in the voxel space. To this end, we propose a novel framework with better utilization of the depth information and fine-grained cross-modal interaction between LiDAR and camera, which consists of two important components. First, a Multi-Depth Unprojection (MDU) method with depth-aware designs is used to enhance the depth quality of the lifted points at each interaction level. Second, a Gated Modality-Aware Convolution (GMA-Conv) block is applied to modulate voxels involved with the camera modality in a fine-grained manner and then aggregate multi-modal features into a unified space. Together they provide the detection head with more comprehensive features from LiDAR and camera. On the nuScenes test benchmark, our proposed method, abbreviated as MSMDFusion, achieves state-of-the-art 3D object detection results with 71.5% mAP and 74.0% NDS, and strong tracking results with 74.0% AMOTA without using test-time-augmentation and ensemble techniques. The code is available at https://github.com/SxJyJay/MSMDFusion.

Authors (6)

Yang Jiao (127 papers)
Zequn Jie (60 papers)
Shaoxiang Chen (24 papers)
Jingjing Chen (99 papers)
Lin Ma (206 papers)
Yu-Gang Jiang (223 papers)

Citations (54)

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Summary

A Critical Assessment of MSMDFusion: Multi-Modal Fusion for 3D Object Detection

The paper "MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection" by Yang Jiao et al. introduces an advanced framework aimed at enhancing the accuracy of 3D object detection by integrating LiDAR and camera data. The work is distinct in its focus on the synergistic use of multi-scale voxel space interactions and features a robust methodology that leverages Multi-Depth Unprojection (MDU) and Gated Modality-Aware Convolution (GMA-Conv).

Methodological Advances

Key components of the proposed method include the MDU and GMA-Conv, both instrumental in refining the fusion process between LiDAR and camera modalities. The MDU approach enhances the reliability of depth estimation for 3D virtual points by employing a K-nearest neighbor strategy rather than a singular nearest neighbor estimation, thus addressing the inherent sparsity discrepancies between LiDAR points and camera pixels. This innovative use of multiple depths for each 2D seed facilitates better spatial accuracy and richer semantic feature integration before unprojection into 3D space.

The GMA-Conv block is designed to perform fine-grained, modality-aware interactions by using the LiDAR data to guide the selective integration of camera information. The gating mechanism optimally adjusts the influence of virtual points while mitigating the computational overhead typically associated with voxel-based processing. Moreover, the scalability of the method is enhanced by the use of a voxel subsampling strategy, charged with selecting reliable reference voxels from the LiDAR data.

Numerical Performance and Comparisons

Empirical validation on the nuScenes benchmark demonstrates that MSMDFusion achieves significant advancements in both mean Average Precision (mAP) and nuScenes Detection Score (NDS), with results at 71.5% mAP and 74.0% NDS, positioning it at the forefront of existing methodologies. It is noteworthy that these results were achieved using considerably fewer virtual points (approximately 16k per frame), a stark contrast to the millions used by competitive methods such as two versions of BEVFusion. This efficiency is further underscored by competitive frame rates and state-of-the-art tracking metrics (AMOTA at 74.0%), underscoring the practicality of the proposed approach.

Theoretical Implications and Future Directions

The reliance on both superior depth estimation techniques and modality-specific feature gating delineates a thought-out strategy towards improving the symbiotic integration of LiDAR and camera data. The outcomes suggest that through judicious use of depth-and-feature enhanced virtual points, the potential exists for even more nuanced system designs that could incorporate real-time adaptation based on environmental contexts or sensor configurations.

The explorations of granularity within multiscale interactions also open avenues for extrapolation into other domains, such as augmented reality and robotics, where multi-modal sensor fusion is key.

Conclusion

MSMDFusion stands as a comprehensive and highly efficient framework that leverages depth optimization and pixel-wise fusion in a multiscale setting, thus advancing the state-of-the-art in 3D object detection. While the paper primarily addresses datasets related to autonomous driving, the scalability and adaptability of the techniques discussed present far-reaching implications, potentially impacting multiple areas engaged in real-time 3D data processing. The integration of flexible and adaptive systems of this nature could significantly enhance the robustness and accuracy of machine perception.

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GitHub - SxJyJay/MSMDFusion: [CVPR 2023] MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection (181 stars)