YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection (2103.09422v1)

Abstract: Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D image-based detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://github.com/Owen-Liuyuxuan/visualDet3D.

• The paper introduces a novel framework that adapts 2D detection methods for efficient stereo 3D object detection by bypassing traditional pseudo-LiDAR processing.
• It employs a lightweight stereo matching module with point-wise correlation, achieving competitive accuracy at over 10 FPS on a single GPU.
• The approach reduces training complexity by eliminating the need for point cloud data, paving the way for cost-effective autonomous systems in resource-constrained environments.

An Analysis of YOLOStereo3D: Efficient Stereo 3D Object Detection

YOLOStereo3D presents an intriguing approach to the problem of 3D object detection using stereo cameras by integrating concepts from efficient 2D object detection methods. This vision-centric approach is particularly significant for low-cost autonomous systems that lack the computational resources for LiDAR-based methods. By revisiting and adapting 2D detection frameworks, the authors propose a solution that enhances 2D methodologies with stereo features, managing to alleviate some computational burdens without sacrificing detection accuracy significantly.

Stereo 3D object detection traditionally involves dense depth reconstruction from disparity estimation, which can be computationally expensive. The novelty here lies in the shift from treating stereo 3D detection as a pseudo-LiDAR problem, which involves transforming image data into 3D point clouds, to an enhanced monocular detection framework. YOLOStereo3D exploits stereo vision in a manner that efficiently produces effective 3D object detections.

The framework incorporates a light-weight stereo matching module to complement the monocular detection backbone from YOLO-style networks. By focusing on a simpler yet robust approach to stereo feature extraction, the method manages to achieve significant computational efficiency. This efficiency stems from using a cost volume based on point-wise correlation, which reduces the computational overhead compared to more traditional concatenation-based methods. This method benefits from producing a thinner feature volume and facilitating substantial feature preservation through hierarchical fusions.

The architecture of YOLOStereo3D is supported by several critical components:

1. Anchor Priors and Filtering: The integration of anchor priors allows the network to utilize statistical depth information effectively, improving the prediction accuracy by leveraging pre-computed depth estimates.
2. Hierarchical Fusion: By implementing a hierarchical structure for stereo feature fusion, the network efficiently uses both fine-grained and semantic information from multi-scale stereo features, which contributes to accurate depth estimation and robust 3D bounding box predictions.
3. Enhanced Stereo Matching Module: Utilizing a point-wise correlation allows the architecture to handle stereo information more efficiently, preserving essential details required for 3D space understanding without overwhelming computational resources.

The empirical results presented demonstrate competitive performance to state-of-the-art methods, with the added advantage of running significantly faster, achieving over ten frames per second on a single GPU. Notably, YOLOStereo3D does not require point cloud data for training, a common dependency in many contemporary frameworks, thereby reducing the complexity associated with model development and deployment.

The implications of these findings suggest promising avenues for practical real-world applications, particularly in resource-constrained environments such as commercial mobile robotic systems and autonomous vehicles operating without expensive sensory equipment. The work alludes to a potential rethinking of stereo-based detection, emphasizing the balance of computational efficiency and detection accuracy which can influence future research toward more accessible machine learning models in computer vision fields.

In conclusion, YOLOStereo3D represents a notable advancement in stereo 3D object detection, demonstrating the power of leveraging stereo vision through a balanced approach rooted in computational pragmatism. Such methodologies pave the way for affordable autonomous solutions, further pushing the boundaries of machine perception and its applications in real-world environments. Further research could explore the integration of additional sensory inputs or improved stereo feature utilization to refine detection accuracy even further, maintaining efficiency.