GEDepth: Bridging the Gap in Monocular Depth Estimation
Table of Contents
Introduction
There is an inherent challenge in monocular depth estimation due to its ill-posed nature, where a single 2D image can originate from an infinite number of 3D scenes.
Despite the substantial advancements seen in leading algorithms within this domain, they primarily cater to a specific combination of visual observations and camera parameters, such as intrinsic and extrinsic. This specificity significantly constrains their applicability in real-world scenarios.
A novel ground embedding module (GEDepth) has been introduced to address this challenge. This module serves the purpose of disentangling camera parameters from visual cues, thus enhancing the model's capacity for generalization.
Given the camera parameters as input, this module generates ground depth information, which is then combined with the input image and utilized in the final depth prediction. A ground attention mechanism is incorporated within the module to fuse the ground depth with residual depth effectively.
Importantly, this ground embedding module is designed to be highly adaptable and lightweight, making it suitable as an add-on component that can be seamlessly integrated into various depth estimation networks.
Ground Embedding
In this section, the discussion commences with an introduction to the concept of ground depth formulation in relation to camera parameters. Subsequently, two distinct designs of ground embedding, which constitute the proposed plug-in module, are presented.
These designs range from an initial depiction of an ideal planar ground (referred to as vanilla) to a more pragmatic representation accounting for undulated ground (referred to as adaptive).
Figure: A schematic overview
Ground Depth: Vanilla
The above figure shows that the process involves initially merging the ground depth information with an unprocessed image to create an input that incorporates ground depth considerations.
The core components of the network consist of an encoder and a decoder, which can be instantiated using established depth estimation models based on either Convolutional Neural Networks (CNNs) or Transformers.
However, it's important to note that the ground depth information is applicable exclusively to the ground area and may not accurately represent non-ground regions.
A ground attention map called "Matten" is introduced to address this limitation. Matten is generated using a series of convolutional layers applied to the ground depth-aware features produced by the encoder.
Each pixel in Matten signifies the likelihood of that pixel belonging to the ground area. This ground attention map is subsequently used to weigh the combination of the ground depth information and its complementary component, which is the residual depth generated by the decoder. This combined information is utilized to produce the final depth prediction.
It's worth emphasizing that the ground attention map is entirely learned implicitly during training and does not rely on additional supervision from external models, such as ground segmentation models.
Instead, the entire network, including the proposed module, is trained solely based on the original depth loss.
Ground Depth: Adaptive
As previously stated, the ground depth pertains to planar ground surfaces. Nonetheless, in real-world driving scenarios, uneven ground is not uncommon, especially in urban settings with inclines and declines in roads, which challenges the assumption of a perfectly flat ground.
An extension to the original vanilla ground depth model is introduced to align the ground depth more closely with actual environments.
Figure: Visual representations of the ground attention maps and the ground slope maps for two scenes from the KITTI test dataset.
Model Evaluation
Comparisons were made between the GEDepth Module and various state-of-the-art methods, as outlined in the table below. Notably, the performance of leading algorithms exhibited a tendency to reach a plateau, with several methods converging to an Absolute Relative Error (Abs Rel) of approximately 0.052.
However, upon incorporating the GEDepth module, there were discernible improvements across a diverse range of commonly used methods.
This underscores the adaptability of the approach to different depth estimation networks. Moreover, it's worth noting that the enhancements achieved by the adaptive module exceeded those of the standard vanilla module, affirming the effectiveness of the approach in modeling ground undulation.
Figure: Comparison between GEDepth and various other SOTA Algorithms.
Conclusion
In the realm of monocular depth estimation, a fundamental challenge stems from its inherent ill-posed nature. This challenge arises because a single 2D image can correspond to an infinite array of 3D scenes.
Despite substantial progress in leading algorithms within this field, they have typically been tailored to specific combinations of visual observations and camera parameters, such as intrinsics and extrinsics. This specificity severely limits their applicability in real-world scenarios.
To address this challenge, the introduction of the ground embedding module, known as GEDepth, offers a promising solution. GEDepth serves the crucial role of disentangling camera parameters from visual cues, thereby enhancing the model's capacity for generalization.
By taking camera parameters as input, GEDepth generates ground depth information, which is seamlessly integrated with the input image and utilized in the final depth prediction. This module has ingeniously incorporated A ground attention mechanism to fuse ground depth with residual depth effectively.
Notably, GEDepth is designed to be both highly adaptable and lightweight, making it a versatile add-on component that can be effortlessly integrated into a variety of depth estimation networks.
The ground embedding journey commences with the formulation of ground depth in relation to camera parameters. Subsequently, two distinctive designs of ground embedding are presented. These designs encompass an initial portrayal of an ideal planar ground (referred to as "vanilla") to a more realistic representation that accounts for undulated ground (referred to as "adaptive").
In the "vanilla" ground depth model, the process entails merging ground depth information with an unprocessed image to create an input that incorporates considerations for ground depth.
This network comprises core components, including an encoder and a decoder, which can be instantiated using established depth estimation models based on either Convolutional Neural Networks (CNNs) or Transformers.
However, it's crucial to recognize that ground depth information applies exclusively to the ground area and may not accurately represent non-ground regions.
To address this limitation, the introduction of a ground attention map named Matten, proves invaluable. Matten is generated using convolutional layers applied to the ground depth-aware features produced by the encoder. Each pixel in Matten signifies the likelihood of belonging to the ground area.
This ground attention map is then utilized to weigh the combination of ground depth information and its complementary component, the residual depth generated by the decoder, to produce the final depth prediction.
Importantly, the ground attention map is learned implicitly during training, eliminating the need for additional supervision from external models like ground segmentation models.
In the "adaptive" ground depth model, which recognizes that real-world ground surfaces are rarely perfectly planar, an extension of the vanilla model is introduced to align ground depth with actual environments better.
In terms of model evaluation, GEDepth underwent comprehensive comparisons with various state-of-the-art methods, as presented in the comparison table.
Furthermore, it's noteworthy that the adaptive module outperformed the standard vanilla module, affirming the effectiveness of the approach in modeling ground undulation.
Frequently Asked Questions (FAQ)
1. What is depth estimation?
Depth estimation involves determining the distance of each pixel in relation to the camera. This information is derived from either single images (monocular) or multiple images (stereo) capturing a scene. Traditional approaches rely on multi-view geometry to establish connections between these images.
2. What is monocular depth estimation?
Monocular depth estimation refers to the process of predicting the depth or distance from the camera for each pixel within a single RGB image (monocular) context. This task, known for its complexity, plays a vital role in applications like 3D scene reconstruction, autonomous driving, and augmented reality, as it enables a deeper understanding of the scene.