Robust and Generalizable Depth Completion Leveraging Single-Image Depth Priors

Core Concepts

A robust and generalizable depth completion method that leverages a single-image depth prediction network as a data-driven prior to complete sparse and noisy depth maps across diverse domains.

Abstract

The paper presents a method for robust and generalizable depth completion that can handle various types of sparse and noisy depth inputs. The key insights are:

Existing depth completion methods are often targeted at specific sparse depth types and do not generalize well across domains. The authors analyze the performance of two state-of-the-art methods and find they are sensitive to mild perturbations in sparsity patterns and noise.

To address this, the authors propose a method that leverages a data-driven single-image depth prediction network as a prior. This prior helps resolve incorrect constraints when there are discrepancies between the sparse depth and the predicted depth.

The authors also introduce effective data augmentation techniques, simulating diverse sparsity patterns during training to improve cross-domain generalization.

To evaluate the robustness and generalization of depth completion methods, the authors redesign two new benchmarks based on typical real-world sparse and noisy depth inputs.

Experiments show the authors' method outperforms state-of-the-art approaches on the new benchmarks and can generalize well to various smartphone-captured depth data, providing a practical solution for high-quality depth sensing on mobile devices.

Stats

Sparse depth maps can have different sparsity patterns, such as uniform sampling, feature-based sampling, and hole-based sampling.
Sparse depth maps can also contain significant noise and outliers due to the depth capture process.

Quotes

"Existing depth completion methods can be classified into two categories according to the input sparsity pattern: depth inpainting methods that fill large holes [9–11], and sparse depth densification methods that densify sparsely distributed depth measurements [12–16]."
"When working on a specific sparsity pattern, e.g., on either NYU [17] or KITTI [18], recent approaches [12, 13, 15, 19, 20] can obtain impressive performance. However, in real-world scenarios, the sparsity pattern may be subject to change or unknown at training time, as it is a function of hardware, software, as well as the configuration of the scene itself."

Key Insights Distilled From

Towards Domain-agnostic Depth Completion

by Guangkai Xu,... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2207.14466.pdf

Deeper Inquiries

How could the proposed depth completion method be extended to handle even more diverse and challenging sparse depth inputs, such as those with large missing regions or extreme noise levels?

The proposed depth completion method could be extended to handle more diverse and challenging sparse depth inputs by incorporating advanced data augmentation techniques during training. This could involve simulating even more extreme sparsity patterns, such as large missing regions or highly noisy depth inputs, to train the model to be robust in such scenarios. By exposing the model to a wide range of challenging conditions during training, it can learn to generalize better and produce accurate depth completions even in the presence of significant missing data or noise.
Additionally, the model could be enhanced with more sophisticated network architectures that are specifically designed to handle missing data and noise. Techniques like graph neural networks or attention mechanisms could be integrated to capture long-range dependencies and contextual information, enabling the model to infer missing depth values more effectively. By leveraging these advanced architectures, the model can better understand the spatial relationships in the scene and make more informed predictions in challenging scenarios.

What other types of data-driven priors, beyond single-image depth prediction, could be leveraged to further improve the robustness and generalization of depth completion models?

Beyond single-image depth prediction, there are several other types of data-driven priors that could be leveraged to enhance the robustness and generalization of depth completion models. One potential approach is to incorporate semantic segmentation information as a prior, where the model uses the semantic labels of the scene to guide the depth completion process. By leveraging semantic information, the model can better understand the scene context and improve the accuracy of depth predictions, especially in complex scenes with multiple objects and structures.
Another valuable data-driven prior could be surface normals estimated from the RGB image. Surface normals provide valuable geometric cues about the scene's structure, which can help the model infer depth information more accurately. By integrating surface normals as an additional input or guidance signal, the depth completion model can benefit from richer geometric information and produce more precise depth estimations, particularly in regions with complex geometry or occlusions.
Furthermore, motion cues from videos or temporal sequences could serve as useful priors for depth completion. By analyzing the motion patterns in consecutive frames, the model can infer depth information based on object movements and scene dynamics. This temporal information can improve the model's ability to handle dynamic scenes and moving objects, enhancing its robustness and generalization capabilities.

Given the practical importance of depth sensing on mobile devices, how could the proposed approach be integrated with other depth sensing modalities (e.g., stereo, ToF) to provide a comprehensive depth capture solution for mobile applications?

To provide a comprehensive depth capture solution for mobile applications, the proposed approach could be integrated with other depth sensing modalities such as stereo vision and Time-of-Flight (ToF) sensors. By combining multiple depth sensing modalities, the mobile device can capture more accurate and detailed depth information, enabling a wide range of applications in augmented reality, 3D reconstruction, and object detection.
One approach to integration is to fuse the depth information obtained from different sensors using sensor fusion techniques. By combining the outputs of the depth completion model with the depth data from stereo vision and ToF sensors, the mobile device can create a more comprehensive and detailed depth map that leverages the strengths of each sensor modality. This fusion process can be done at different levels, such as pixel-level fusion or feature-level fusion, to ensure a seamless integration of the depth information.
Furthermore, the proposed approach can be optimized for real-time performance on mobile devices by leveraging hardware acceleration and efficient algorithms. By optimizing the model architecture and inference process for mobile platforms, the depth completion solution can run efficiently on smartphones and tablets, enabling on-device depth sensing capabilities without the need for cloud processing.
Overall, integrating the proposed depth completion approach with other depth sensing modalities can provide a powerful and versatile depth capture solution for mobile applications, enhancing the user experience and enabling a wide range of innovative applications in mobile augmented reality and computer vision.