SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation (2204.03636v3)

Abstract: Depth estimation from images serves as the fundamental step of 3D perception for autonomous driving and is an economical alternative to expensive depth sensors like LiDAR. The temporal photometric constraints enables self-supervised depth estimation without labels, further facilitating its application. However, most existing methods predict the depth solely based on each monocular image and ignore the correlations among multiple surrounding cameras, which are typically available for modern self-driving vehicles. In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. We apply cross-view self-attention to efficiently enable the global interactions between multi-camera feature maps. Different from self-supervised monocular depth estimation, we are able to predict real-world scales given multi-camera extrinsic matrices. To achieve this goal, we adopt the two-frame structure-from-motion to extract scale-aware pseudo depths to pretrain the models. Further, instead of predicting the ego-motion of each individual camera, we estimate a universal ego-motion of the vehicle and transfer it to each view to achieve multi-view ego-motion consistency. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets DDAD and nuScenes.

• The paper introduces a novel self-supervised method using a cross-view transformer to integrate surrounding camera views for depth estimation.
• It employs a unified network with multi-scale interactions and skip connections to achieve scale-aware predictions beyond traditional monocular techniques.
• Experiments on DDAD and nuScenes demonstrate state-of-the-art improvements in Absolute Relative Error and RMSE, enhancing autonomous driving safety.

Essay: SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation

The paper "SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation" presents a sophisticated approach to depth estimation employing self-supervised learning in the context of autonomous driving. The research leverages the typically available multi-camera setups in modern vehicles to improve depth prediction accuracy, which is critical for 3D perception.

The authors introduce a novel methodology termed SurroundDepth, designed to integrate and process data from multiple surrounding camera views to generate depth maps. A significant advancement in this work is the utilization of a cross-view transformer to facilitate efficient information fusion across views. The cross-view transformer executes self-attention mechanisms, which allow for robust global feature interactions across multi-camera inputs, improving upon traditional monocular depth prediction approaches that generally consider each camera's input in isolation.

Key innovations in the SurroundDepth method include the joint processing of input features from multiple surrounding cameras using a unified network, and the implementation of cross-view self-attention mechanisms. This structure allows for enhanced understanding of spatial relationships in diverse perspectives, leading to improvements in real-world scale prediction, a capability often unmet by monocular systems due to their scale ambiguity.

The neural architecture integrates multi-scale interactions, achieved through downsampling with depthwise separable convolutions and upsampling via deconvolutions, coupled with skip connections to preserve and refine feature details. This multi-scale strategy is vital for handling varying object sizes and optimizing computational efficiency.

To ensure scale-awareness, typically elusive in monocular systems, the paper proposes pretraining models using pseudo depths derived from two-frame Structure-from-Motion (SfM), accompanied by a novel joint pose estimation technique. This framework provides a scale-calibrated depth learning process, where multi-camera extrinsic matrices enable coherent scale predictions across views.

The paper reports state-of-the-art performance on prominent datasets for multi-camera depth estimation, namely DDAD and nuScenes. Experimental validation shows that SurroundDepth significantly surpasses existing methods that treat each camera angle independently without exploiting cross-view spatial coherence. Notably, the model achieves improvements in metrics such as Absolute Relative Error and RMSE when evaluated under a unified framework that adheres to the geometric constraints inherent in real-world environments.

Largely, the work emphasizes the practical implications of enhanced depth map accuracy for autonomous driving, suggesting that future depth estimation systems should incorporate multi-camera correlations to ensure superior environmental understanding and automotive safety. Furthermore, the paper opens avenues for future research in complex scene mapping and real-time depth processing in dynamic conditions, with potential applications extending beyond vehicular technology into robotics and augmented reality.

In conclusion, SurroundDepth offers a compelling argument for a paradigm shift towards multi-camera integration in autonomous systems, demonstrating the dual benefits of increased accuracy and scale-awareness in depth estimation tasks. This work advances the domain by providing a scalable methodology that could form the basis for more reliable and intelligent perception models in next-generation autonomous systems.