CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection (2209.13507v3)

Abstract: To achieve accurate 3D object detection at a low cost for autonomous driving, many multi-camera methods have been proposed and solved the occlusion problem of monocular approaches. However, due to the lack of accurate estimated depth, existing multi-camera methods often generate multiple bounding boxes along a ray of depth direction for difficult small objects such as pedestrians, resulting in an extremely low recall. Furthermore, directly applying depth prediction modules to existing multi-camera methods, generally composed of large network architectures, cannot meet the real-time requirements of self-driving applications. To address these issues, we propose Cross-view and Depth-guided Transformers for 3D Object Detection, CrossDTR. First, our lightweight depth predictor is designed to produce precise object-wise sparse depth maps and low-dimensional depth embeddings without extra depth datasets during supervision. Second, a cross-view depth-guided transformer is developed to fuse the depth embeddings as well as image features from cameras of different views and generate 3D bounding boxes. Extensive experiments demonstrated that our method hugely surpassed existing multi-camera methods by 10 percent in pedestrian detection and about 3 percent in overall mAP and NDS metrics. Also, computational analyses showed that our method is 5 times faster than prior approaches. Our codes will be made publicly available at https://github.com/sty61010/CrossDTR.

• The paper introduces CrossDTR, a framework that fuses cross-view transformers with sparse depth embeddings for precise 3D object detection.
• It employs a lightweight depth predictor that builds accurate sparse depth maps without extra training data, reducing computational load.
• Empirical results show a 10% increase in pedestrian detection precision and a 5x speed boost, outperforming traditional multi-camera methods.

Overview of CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection

The paper by Tseng et al. introduces CrossDTR, a novel 3D object detection framework specifically designed for autonomous driving systems requiring cost-effective yet precise object detection capabilities. Current multi-camera systems aim to solve occlusion problems faced by monocular methods; however, they often struggle with inaccurate depth estimation, resulting in numerous false positive bounding boxes especially for small objects such as pedestrians. The proposed CrossDTR framework addresses these deficiencies by integrating cross-view and depth-guided transformers, innovatively combining depth hints with multi-camera image data.

Key Contributions

1. Lightweight Depth Predictor: The paper introduces a depth predictor that constructs precise object-wise sparse depth maps and low-dimensional depth embeddings without the need for additional depth datasets during training. This design is pivotal for enhancing depth estimation accuracy without imposing a significant computational burden, thereby meeting the real-time operating requirements of autonomous vehicles.
2. Cross-view Depth-guided Transformer: The CrossDTR leverages a unique cross-view depth-guided transformer, enabling effective fusion of visual and depth data across multiple camera perspectives to produce accurate 3D bounding boxes. This fusion process is managed by a transformer architecture that efficiently reduces complex high-dimensional information, capturing cross-view spatial context necessary for precise small object detection.
3. Enhanced Performance: The methods described in the paper achieve superior performance compared to current multi-camera approaches, as evidenced by a 10% improvement in pedestrian detection precision and a 3% increase in mean Average Precision (mAP) and nuScenes detection score (NDS). Additionally, CrossDTR is demonstrated to be five times faster than existing solutions, which underscores its practical applicability for real-world deployment.

Implications and Future Directions

The research presented in this paper has significant implications for the field of autonomous vehicle perception. Notably, it provides a robust framework for incorporating depth information into camera-based 3D object detection systems without necessitating high-cost LiDAR or stereo vision inputs. This advancement is crucial for deploying self-driving technologies at scale without excessive hardware expenditures.

Furthermore, the paper potentially opens new avenues for enhancing AI-driven perception systems in terms of speed and accuracy, with the cross-view and depth-guided approach likely inspiring further exploration into low-cost, high-performance detection models. Future work may involve extending this framework to incorporate temporal integration of depth data, as suggested by advances in video-based detection methodologies. Additionally, as self-supervised and unsupervised learning techniques gain traction, integrating such approaches with CrossDTR may further reduce dependency on labeled datasets, driving down costs while enhancing scalability and adaptability.

Overall, the methodology and results presented by Tseng et al. represent a substantial contribution to the development of cost-effective, high-accuracy autonomous systems, setting a benchmark against which future research may be measured.