MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones (2207.12716v1)

Abstract: In this technical report, we present our solution, dubbed MV-FCOS3D++, for the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022. For multi-view camera-only 3D detection, methods based on bird-eye-view or 3D geometric representations can leverage the stereo cues from overlapped regions between adjacent views and directly perform 3D detection without hand-crafted post-processing. However, it lacks direct semantic supervision for 2D backbones, which can be complemented by pretraining simple monocular-based detectors. Our solution is a multi-view framework for 4D detection following this paradigm. It is built upon a simple monocular detector FCOS3D++, pretrained only with object annotations of Waymo, and converts multi-view features to a 3D grid space to detect 3D objects thereon. A dual-path neck for single-frame understanding and temporal stereo matching is devised to incorporate multi-frame information. Our method finally achieves 49.75% mAPL with a single model and wins 2nd place in the WOD challenge, without any LiDAR-based depth supervision during training. The code will be released at https://github.com/Tai-Wang/Depth-from-Motion.

• The paper introduces MV-FCOS3D++ that integrates pretrained monocular backbones with multi-view transformations to enhance 4D object detection accuracy.
• It converts 2D features into a unified 3D voxel grid using stereo cues, improving object localization without LiDAR data.
• Evaluated on the Waymo dataset, the framework achieves a mean Average Precision Level of 49.75%, demonstrating strong performance in camera-only detection.

MV-FCOS3D++: Advancements in Multi-View Camera-Only 4D Object Detection

The paper "MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones" presents a robust framework for advancing the field of camera-based object detection, particularly in the context of autonomous driving. This research demonstrates how multi-view camera setups can effectively localize and classify 3D objects without reliance on LiDAR data, by leveraging pretrained monocular backbones and enhanced temporal modeling techniques.

Methodological Overview

The MV-FCOS3D++ framework is a multi-view perception system that builds upon a monocular detector, FCOS3D++, incorporating multi-view and temporal stereo cues to bolster performance in 4D object detection tasks. This approach is tailored to address the requirements of the Waymo Open Dataset Challenge 2022, particularly focusing on camera-only data.

1. Pretraining and Monocular Backbones: The method begins with pretraining a 2D feature extractor using FCOS3D++, which is solely reliant on object annotations from the Waymo dataset. This pretraining enhances the system's ability to interpret semantic and geometric details within monocular images. The pretrained backbone is subsequently fine-tuned within the full MV-FCOS3D++ structure for superior object detection in three-dimensional spaces.
2. Multi-View Feature Transformation: By transforming features from multiple camera views into a 3D voxel grid, the framework bridges monocular feature extraction with volume-based detections. The transformation leverages camera parameters to project 2D features into a unified 3D space, where stereo cues from overlapping views aid accurate object localization.
3. 4D Detection via Dual-Path Modeling: The framework introduces a dual-path neck that disentangles single-frame understanding from temporal stereo matching. This design enables integration of multi-frame information to improve depth estimation while maintaining robust performance in scenarios where stereo cues might fail, such as static or independently moving objects.

Performance Evaluation

The efficacy of MV-FCOS3D++ is quantitatively validated on the challenging Waymo dataset. Notably, the framework achieves a mean Average Precision Level (mAPL) of 49.75% with a single model in the test evaluation, securing second place in the camera-only 3D detection track. This performance is indicative of the model's capability to leverage camera-only data efficiently, reflecting notable improvements particularly in detecting cars.

Implications and Future Directions

The implications of this research are significant for the autonomous driving domain, where reliance on expensive LiDAR systems may be mitigated by advanced camera-based methods. By refining multi-view and temporal processing techniques, MV-FCOS3D++ suggests a viable pathway forward for more cost-effective object detection systems in autonomous vehicles.

Looking forward, advancements in neural architectures for multi-view processing, improvements in monocular training schemes, and innovations in temporal stereo matching could further enhance the capabilities demonstrated by MV-FCOS3D++. Expanding the framework's application to handle diverse environmental conditions and continuing to optimize processing efficiency will be key areas for future exploration.

In summary, the lessons drawn from the development and evaluation of MV-FCOS3D++ highlight the untapped potential of monocular and multi-view camera systems in complex 3D environments, aligning with broader trends of diversification and cost reduction in sensor technology for autonomous systems.