Semantically-Guided Representation Learning for Self-Supervised Monocular Depth (2002.12319v1)

Abstract: Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.

• The paper introduces a novel architecture that uses pretrained semantic networks with pixel-adaptive convolutions to enhance depth prediction accuracy.
• It employs a two-stage training protocol to mitigate infinite depth artifacts from dynamic objects, significantly improving robustness on the KITTI benchmark.
• Experiments validate that the method achieves superior performance in capturing fine-grained features and ambiguous photometric cues in self-supervised settings.

Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

The paper explores innovative methodologies in the domain of self-supervised monocular depth estimation, where the primary objective is to enhance the interpretability and accuracy of depth prediction using monocular imagery. As a salient problem in computer vision, achieving high precision in depth estimates is crucial for applications such as autonomous driving, robotics, and augmented reality.

Contributions

The authors introduce a novel architecture that harnesses pretrained semantic segmentation networks, aiming to guide geometric representation learning without deviating from the self-supervised regime. Unlike previous methodologies that incorporate semantic information with strong supervised signals or multitask objectives, this approach leverages semantic networks via pixel-adaptive convolutions to influence depth features. This method maintains the integrity of self-supervised learning by utilizing semantic supervision indirectly, effectively aligning geometric representations with high-level semantic understanding.

Moreover, the authors present a two-stage training protocol designed to address biases related to dynamic object depth estimation, particularly focusing on the infinite depth issue commonly arising in scenarios where objects move at a similar velocity to the capturing camera. By resampling the dataset to exclude problematic samples, the model's robustness to such biases is significantly enhanced.

Methodology

1. Pixel-Adaptive Convolutions: These are employed to allow more contextual and content-driven convolutional computations, thereby distinguishing between semantically distinct regions and leading to more precise depth and boundary predictions.
2. Semantic Features: The implementation relies on a fixed, pretrained semantic segmentation network to derive multi-level feature maps. This auxiliary network remains unaltered during training, and its outputs inform the depth model via structured guidance, thus circumventing the need for explicit semantic labels or losses.
3. Two-Stage Training: A depth network is initially trained using the entire dataset. Depth maps exhibiting the infinite depth issue are identified and used to filter the training set, allowing for re-training with a reduced, unbiased dataset. This process ensures that dynamic elements are more accurately modeled, improving overall depth prediction.

Evaluation

Experiments conducted on the KITTI benchmark dataset demonstrate that the proposed semantically-guided architecture surpasses the state-of-the-art in self-supervised monocular depth estimation. Critically, the approach achieves superior performance across general evaluations and notably enhances predictions in challenging scenarios involving fine-grained features and ambiguous photometric cues.

Quantitative results show the depth network's proficiency not merely in average errors over all pixels but in category-specific assessments, indicating consistent improvements across varied semantic classes. This suggests enhanced capability in handling diverse scene compositions and object categories.

Implications and Future Work

This work reinforces the potential of integrating semantic priors into depth learning frameworks without departing from a self-supervised context. By leveraging semantic networks, the approach demonstrates how depth models can achieve higher accuracy and robustness. One clear trajectory for future research involves exploring other semantic attributes to further refine depth predictions, such as integrating instance-level segmentation or complementary modalities like optical flow. Additionally, extending the methodology to accommodate varying environmental conditions and less-structured input data would be advantageous for further real-world applications.

In conclusion, this research marks a significant step in merging semantic understanding with geometric learning, offering promising directions for subsequent advancements in self-supervised depth estimation paradigms.