TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation (2412.20911v1)

Abstract: Accurate multi-view 3D object detection is essential for applications such as autonomous driving. Researchers have consistently aimed to leverage LiDAR's precise spatial information to enhance camera-based detectors through methods like depth supervision and bird-eye-view (BEV) feature distillation. However, existing approaches often face challenges due to the inherent differences between LiDAR and camera data representations. In this paper, we introduce the TiGDistill-BEV, a novel approach that effectively bridges this gap by leveraging the strengths of both sensors. Our method distills knowledge from diverse modalities(e.g., LiDAR) as the teacher model to a camera-based student detector, utilizing the Target Inner-Geometry learning scheme to enhance camera-based BEV detectors through both depth and BEV features by leveraging diverse modalities. Specially, we propose two key modules: an inner-depth supervision module to learn the low-level relative depth relations within objects which equips detectors with a deeper understanding of object-level spatial structures, and an inner-feature BEV distillation module to transfer high-level semantics of different key points within foreground targets. To further alleviate the domain gap, we incorporate both inter-channel and inter-keypoint distillation to model feature similarity. Extensive experiments on the nuScenes benchmark demonstrate that TiGDistill-BEV significantly boosts camera-based only detectors achieving a state-of-the-art with 62.8% NDS and surpassing previous methods by a significant margin. The codes is available at: https://github.com/Public-BOTs/TiGDistill-BEV.git.

Overview of "TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation"

The paper introduces TiGDistill-BEV, a novel approach in the domain of multi-view Bird's Eye View (BEV) 3D object detection, aiming to enhance camera-based detectors by leveraging the advantages of both camera and LiDAR modalities. This research addresses the significant representation differences between the data collected via LiDAR and cameras, which have traditionally posed challenges for effective sensor integration. The primary innovation of TiGDistill-BEV lies in its two key modules which tackle these differences through Knowledge Distillation (KD): the inner-depth supervision module and the inner-feature BEV distillation module.

Methodology

1. Inner-depth Supervision Module:
   • This module is tailored to learn relative depth relations at an object level within images. By selecting an adaptive reference point for objects, this method calculates continuous depth values, which enhance depth map predictions by capturing local geometric structures. This module provides the camera model with more accurate geometric understanding, which is crucial for generating high-quality BEV representations.
2. Inner-feature BEV Distillation Module:
   • Unlike conventional dense feature mimicking, this module employs sparsely sampled keypoints from the teacher model's BEV space to guide the student. By focusing on high-level semantics, this approach alleviates the cross-modal semantic gap between LiDAR and camera-based representations. It does so through inter-channel and inter-keypoint distillation methods, providing a robust framework for transferring part-wise feature knowledge.

These modules collectively aim to integrate the geometric precision viable from LiDAR into a camera-only detection framework, thus optimizing object detection accuracy and reliability.

Experimental Results

The experimental validation of TiGDistill-BEV leveraged the comprehensive nuScenes dataset, a benchmark in autonomous driving research. The system demonstrated substantial improvements over baseline models, with TiGDistill-BEV enhancing BEVDepth's NDS (nuScenes Detection Score) to 62.8% and achieving a mean Average Precision (mAP) of 53.9%. These gains underscore the efficacy of the proposed framework in distilling cross-modal knowledge, offering significant advancements in the camera-based detection domain.

Implications and Future Directions

TiGDistill-BEV exemplifies the potential for intelligent sensor integration, specifically highlighting the synergy between LiDAR and camera-based data representations. By integrating detailed inner-geometric features distilled from a teacher model, the framework not only enhances the geometric fidelity of camera-based models but also sets a precedent for future advancements in multi-sensor fusion.

The enhancements facilitated by TiGDistill-BEV indicate promising future applications in areas where sensor cost and computational efficiency are pivotal, such as autonomous vehicles and robotics. Future research could focus on further reducing the domain gap, exploring real-time implementations, and integrating with other modalities, such as radar, to expand operational robustness across diverse environmental conditions. The progress represented by TiGDistill-BEV also invites exploration into broader applications of cross-modal knowledge distillation, beyond traditional object detection tasks.