Self-Supervised Monocular Depth Estimation with Internal Feature Fusion (2110.09482v3)

Abstract: Self-supervised learning for depth estimation uses geometry in image sequences for supervision and shows promising results. Like many computer vision tasks, depth network performance is determined by the capability to learn accurate spatial and semantic representations from images. Therefore, it is natural to exploit semantic segmentation networks for depth estimation. In this work, based on a well-developed semantic segmentation network HRNet, we propose a novel depth estimation network DIFFNet, which can make use of semantic information in down and upsampling procedures. By applying feature fusion and an attention mechanism, our proposed method outperforms the state-of-the-art monocular depth estimation methods on the KITTI benchmark. Our method also demonstrates greater potential on higher resolution training data. We propose an additional extended evaluation strategy by establishing a test set of challenging cases, empirically derived from the standard benchmark.

• The paper introduces DIFFNet, a novel network that fuses multi-scale features to bridge the semantic gap in self-supervised depth estimation.
• It leverages an attention-based decoder and HRNet to refine depth maps and outperforms previous methods on the KITTI benchmark.
• The research demonstrates robustness with high-resolution inputs and paves the way for advanced self-supervised learning in autonomous navigation.

Overview of Self-Supervised Monocular Depth Estimation with Internal Feature Fusion

The paper "Self-Supervised Monocular Depth Estimation with Internal Feature Fusion" introduces a novel network, termed DIFFNet, aimed at advancing the field of monocular depth estimation through self-supervised learning. This research is built on the hypothesis that enhancing the semantic and spatial representations can significantly improve depth estimation from a single image, which is a critical task in autonomous systems, robotics, and 3D reconstruction.

Methodology

The proposed DIFFNet leverages the HRNet, a semantic segmentation network known for capturing high-resolution representations. The authors apply a feature fusion mechanism and an attention module to seamlessly integrate semantic information during both the encoder and decoder stages of the network. The key innovation lies in utilizing internal feature fusion, which combines multi-stage features in the encoder, hence mitigating the semantic gap between multi-scale feature maps.

The authors structured the DIFFNet to consist of:

1. Multi-Stage Feature Fusion: A concatenation strategy utilized within the encoder, enabling richer semantic representations without amplifying the computational costs. This facilitates bridging the gap between the low-level spatial information and high-level semantic features.
2. Attention-Based Decoder: Incorporating a channel attention mechanism, the decoder effectively refines features acquired from encoder stages, enhancing the restoration of depth information with precise object boundaries.

The DIFFNet architecture is evaluated under the SfM framework that constitutes depth and pose networks, which infer depth and view transformations self-supervisedly from a sequence of images by synthesizing views via photometric consistency.

Experimental Results

The network's performance is benchmarked against the KITTI dataset, widely utilized in monocular depth estimation tasks. DIFFNet demonstrates superior performance, with substantial improvement in metrics like Absolute Relative Error and RMSE over prior state-of-the-art self-supervised methods. Notably, DIFFNet exhibits increased efficacy with higher resolution data, emphasizing its robustness in extracting and processing high-fidelity depth maps.

Moreover, an innovative evaluation strategy is introduced, focusing on a test set derived from challenging cases within the KITTI benchmark. This extended evaluation underscores DIFFNet's resiliency in complex scenes, showcasing a balanced performance not just on average but also on hard-to-infer image data.

Implications and Future Directions

The implications of this research are profound, given the enhanced capacity for self-supervised monocular depth estimation to process information with minimal labeled data. Practical applications span autonomous navigation systems, especially where computational resources and real-time performance are critical.

Theoretical implications suggest a move toward networks that inherently capture and utilize semantic richness, motivating further exploration into architectures that blend spatial precision with semantic depth. Future developments might explore extending this architecture to multi-task learning scenarios where shared representations could benefit related vision tasks like segmentation or object detection.

The research opens avenues for further integrating specialized modules, such as dynamic object handling or temporal consistency mechanisms, potentially benefiting scenarios involving non-static scenes.

Overall, this work significantly contributes to the dialogue on how best to amalgamate internal feature representations for improved depth perception capability within self-supervised frameworks. The methodologies presented can inspire subsequent models aimed at overcoming the limitations of current depth estimation techniques.