TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning (2212.13979v1)

Abstract: To achieve accurate and low-cost 3D object detection, existing methods propose to benefit camera-based multi-view detectors with spatial cues provided by the LiDAR modality, e.g., dense depth supervision and bird-eye-view (BEV) feature distillation. However, they directly conduct point-to-point mimicking from LiDAR to camera, which neglects the inner-geometry of foreground targets and suffers from the modal gap between 2D-3D features. In this paper, we propose the learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features, termed as TiG-BEV. First, we introduce an inner-depth supervision module to learn the low-level relative depth relations between different foreground pixels. This enables the camera-based detector to better understand the object-wise spatial structures. Second, we design an inner-feature BEV distillation module to imitate the high-level semantics of different keypoints within foreground targets. To further alleviate the BEV feature gap between two modalities, we adopt both inter-channel and inter-keypoint distillation for feature-similarity modeling. With our target inner-geometry distillation, TiG-BEV can effectively boost BEVDepth by +2.3% NDS and +2.4% mAP, along with BEVDet by +9.1% NDS and +10.3% mAP on nuScenes val set. Code will be available at https://github.com/ADLab3Ds/TiG-BEV.

• The paper introduces a dual-component scheme that enhances camera-based 3D detection via inner-depth supervision and BEV feature distillation.
• It reports significant improvements on the nuScenes dataset, achieving notable gains in NDS and mAP over baseline methods.
• The method bridges the gap between LiDAR and camera modalities, paving the way for cost-effective solutions in autonomous driving.

An Expert Analysis of TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning

TiG-BEV represents an innovative approach to enhancing multi-view BEV 3D object detection, addressing critical challenges posed by the modal gap between LiDAR and camera-based systems. This methodology explores the underutilized aspect of target inner-geometry learning to improve the performance of camera-based detectors, which are inherently limited by their lack of geometric depth cues when compared to their LiDAR-based counterparts.

Methodological Innovations

The paper presents a robust solution by proposing a dual-component learning scheme. First, the inner-depth supervision module is introduced to enhance the network's understanding of spatial structures within detected objects. This is achieved through continuous depth value calculations and the use of an adaptive reference point that allows for the modeling of relative depth relations among foreground pixels. By moving beyond absolute depth values, the authors aim to capture fine-grained structural information that is critical for accurate 3D detection.

The second component, the inner-feature BEV distillation module, transfers semantic information from a pre-trained LiDAR-based detector to a camera-based one. Instead of direct feature-to-feature mimicry, which might suffer from cross-modal semantic discrepancies, this approach employs inter-channel and inter-keypoint distillation strategies. These refine the process by focusing on the feature similarity modeling to preserve the idiosyncratic characteristics of each modality while promoting a more effective knowledge transfer.

Empirical Results

The authors report significant gains in detection accuracy on the nuScenes dataset, with enhancements such as a +2.3\% NDS and +2.4\% mAP for BEVDepth, and more pronounced improvements like +9.1\% NDS and +10.3\% mAP for BEVDet when CBGS is not implemented. These metrics underscore TiG-BEV's efficacy in improving the baseline models and demonstrate its potential for broader applications.

Implications and Future Directions

From a theoretical standpoint, TiG-BEV advances the landscape of cross-modal learning in 3D object detection by illustrating the benefits of capturing intra-object geometric features. This could inspire further research into techniques that bridge perceptual gaps between data modalities, enhancing holistic scene understanding.

Practically, the method shows promise for deployment in cost-sensitive applications, such as autonomous driving, where reliance on camera-based detection is preferred due to lower costs and ease of integration. By effectively leveraging LiDAR-rich geometrical semantics, TiG-BEV manages to elevate camera-only setups without the additional hardware burdens.

Looking ahead, TiG-BEV could catalyze developments in unified perception systems that seamlessly incorporate multiple sensor modalities. Future work could explore extending the methodology to joint modality learning, potentially benefiting a broad spectrum of AI-driven real-world applications.

In conclusion, TiG-BEV marks a significant step in the refinement of camera-based 3D object detection, illuminating paths for future innovations in computer vision by harnessing the ontological depth of target inner-geometries.