SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving (2008.04017v3)

Abstract: State-of-the-art self-supervised learning approaches for monocular depth estimation usually suffer from scale ambiguity. They do not generalize well when applied on distance estimation for complex projection models such as in fisheye and omnidirectional cameras. This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images. Our contribution to this work is threefold: Firstly, we introduce a novel distance estimation network architecture using a self-attention based encoder coupled with robust semantic feature guidance to the decoder that can be trained in a one-stage fashion. Secondly, we integrate a generalized robust loss function, which improves performance significantly while removing the need for hyperparameter tuning with the reprojection loss. Finally, we reduce the artifacts caused by dynamic objects violating static world assumptions using a semantic masking strategy. We significantly improve upon the RMSE of previous work on fisheye by 25% reduction in RMSE. As there is little work on fisheye cameras, we evaluated the proposed method on KITTI using a pinhole model. We achieved state-of-the-art performance among self-supervised methods without requiring an external scale estimation.

• The paper presents a novel network architecture that synergizes semantic segmentation with self-supervised monocular fisheye distance estimation.
• The paper employs a robust, adaptive loss function that improves estimation accuracy by reducing RMSE by 25% without extensive hyperparameter tuning.
• The paper introduces semantic masking to ignore dynamic objects, thereby enhancing the reliability of depth predictions in challenging driving scenarios.

Overview of SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving

The paper presents an innovative approach to improving self-supervised monocular distance estimation in challenging camera geometries such as fisheye and pinhole cameras, addressing a critical component in autonomous driving and similar domains. The methodology introduced, termed SynDistNet, integrates semantic segmentation into the self-supervised learning process, forming a multi-task learning framework aimed at enhancing the accuracy and robustness of distance estimation.

Main Contributions

1. Network Architecture: The authors develop a novel distance estimation network, incorporating a self-attention-based encoder. This model utilizes semantic feature guidance within the decoder, enabling the network to refine its distance predictions through a concurrent understanding of semantic content. This architecture facilitates one-stage training, optimizing both tasks simultaneously without necessitating separate pre-training steps.
2. Robust Loss Function: A generalized robust loss function is integrated into the training pipeline, further enhancing the network's performance. This function replaces traditional $L_1$ loss functions with a loss that can be adaptively optimized according to the data, thereby negating the need for intricate hyperparameter tuning typically associated with reprojection losses.
3. Semantic Masking: A mechanism to mitigate the effects of dynamic objects that violate the static world assumption, which is a common challenge in depth estimation tasks, is introduced. By employing a semantic masking strategy, the network can effectively ignore non-static objects during training, thus reducing errors in distance predictions.

Experimental Results

The SynDistNet framework demonstrates significant improvements over previous methods, achieving a 25% reduction in RMSE for fisheye distance estimation. The authors validate their method by conducting experiments on standard datasets such as KITTI and WoodScape, showcasing state-of-the-art performance without relying on external scale estimation.

Theoretical and Practical Implications

The fusion of semantic segmentation and distance estimation introduces an innovative perspective on how these tasks can reinforce each other. By leveraging semantically informed distance estimates, SynDistNet is able to produce more accurate and reliable predictions, especially in complex scenes where traditional depth estimation techniques might falter.

The practical implications of this research are vast, particularly in autonomous driving, where understanding the scale and distance of objects and elements within a scene is vital for navigation and decision-making. The proposed approach could be extended to other domains requiring robust perception systems, such as robotics and augmented reality.

Future Directions

This research opens several avenues for future exploration:

• Extension to Other Camera Models: Investigating the adaptability of the proposed methodology to other unconventional camera geometries beyond fisheye and pinhole models.
• Real-Time Implementation: Optimizing the current framework for real-time performance on edge devices, which is crucial for real-world deployment in autonomous systems.
• Integration with More Complex Semantic Tasks: Further integrating the network with additional semantic tasks, such as instance segmentation or object detection, to leverage even richer contextual information.

Overall, this work represents a significant step forward in leveraging semantic segmentation to enhance distance estimation, a critical capability for the advancement of machine perception technologies in complex, dynamic environments.