RDFC-GAN: An Effective RGB-Depth Fusion Model for Completing Indoor Depth Maps

Core Concepts

RDFC-GAN, a novel two-branch end-to-end network, effectively fuses raw depth maps and RGB images to generate dense and detailed completed depth maps for indoor environments, outperforming state-of-the-art depth completion methods.

Abstract

The paper proposes a novel depth completion method named RDFC-GAN that effectively fuses raw depth maps and RGB images to generate dense and detailed completed depth maps for indoor environments.
The model consists of two main branches:

Manhattan-Constraint Network (MCN) Branch:

Utilizes a Manhattan normal module to leverage the Manhattan world assumption in indoor scenes, generating a normal map to guide the depth completion.
Employs an encoder-decoder structure to regress the local dense depth values from the raw depth map.

RGB-Depth Fusion CycleGAN (RDFC-GAN) Branch:

Uses a CycleGAN-based structure to translate RGB imagery into detailed, textured depth maps while ensuring high fidelity through cycle consistency.
Fuses the depth and RGB features through adaptive fusion modules named W-AdaIN.
The model is trained with pseudo depth maps that mimic the missing patterns in indoor depth data. Comprehensive evaluations on NYU-Depth V2 and SUN RGB-D datasets show that RDFC-GAN significantly outperforms state-of-the-art depth completion methods, especially in realistic indoor settings.

Stats

Raw depth maps captured by indoor depth sensors often exhibit extensive missing values due to sensor limitations and scene properties.
Existing depth completion methods may fail to handle large contiguous regions of missing depth values prevalent in indoor environments.
Downsampling-based evaluation settings used in prior works do not reflect the real missing patterns in indoor depth data.

Quotes

"Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments."
"Prevailing depth completion approaches [11]–[13], [19], [22] emphasize intricate adaptive propagation structures for local pixels, which may fail in dealing with large invalid depth maps that are prevalent in indoor scenes."
"It is unclear whether the successful methods in uniformly sparse depth map settings still win in indoor depth completion tasks."

Key Insights Distilled From

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

by Haowen Wang,... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2306.03584.pdf

Deeper Inquiries

How can the proposed RDFC-GAN model be extended to handle outdoor depth completion scenarios with different missing patterns

To extend the RDFC-GAN model for outdoor depth completion scenarios with different missing patterns, several modifications and adaptations can be made:

Data Augmentation: Incorporate outdoor-specific data augmentation techniques to simulate missing patterns commonly found in outdoor environments, such as occlusions from foliage, shadows, or reflections from water bodies.

Adaptive Fusion Modules: Develop adaptive fusion modules that can dynamically adjust to varying outdoor conditions, such as changing lighting conditions, different surface textures, and reflective surfaces commonly found outdoors.

Multi-Sensor Fusion: Integrate data from multiple sensors, such as LiDAR, thermal cameras, or event-based cameras, to capture a more comprehensive understanding of the outdoor scene and improve depth completion accuracy.

Contextual Information: Incorporate contextual information specific to outdoor environments, such as scene semantics, weather conditions, and time of day, to enhance the model's ability to predict missing depth values accurately.

Transfer Learning: Pre-train the model on a diverse outdoor dataset to adapt it to outdoor depth completion tasks and fine-tune it on specific outdoor scenarios to improve performance.

By incorporating these adaptations, the RDFC-GAN model can be extended to handle outdoor depth completion scenarios effectively, catering to the unique challenges and missing patterns present in outdoor environments.

What other geometric constraints or priors beyond the Manhattan world assumption could be incorporated to further improve the depth completion performance in diverse indoor environments

In addition to the Manhattan world assumption, several other geometric constraints or priors can be incorporated to further enhance depth completion performance in diverse indoor environments:

Planar Surfaces: Exploit the assumption of planar surfaces commonly found in indoor environments to guide depth completion. By leveraging the regularity of walls, floors, and ceilings, the model can make more accurate depth predictions.

Object Shape Priors: Integrate prior knowledge about common object shapes and sizes in indoor scenes to improve depth completion accuracy. Understanding the typical dimensions of furniture, appliances, and fixtures can aid in filling missing depth values.

Symmetry Constraints: Utilize symmetry constraints to enforce symmetrical properties in depth completion, especially for objects or structures that exhibit symmetrical characteristics in indoor environments.

Depth Discontinuities: Consider depth discontinuities at object boundaries and edges to enhance the sharpness and accuracy of depth completion results, ensuring smooth transitions between different depth values.

Lighting Conditions: Incorporate information about lighting conditions and shadows to adjust depth predictions accordingly, taking into account how light and shadow affect depth perception in indoor environments.

By incorporating these additional geometric constraints or priors, the RDFC-GAN model can further improve its depth completion performance in diverse indoor environments.

How can the RDFC-GAN model be adapted to leverage additional sensor modalities, such as thermal or event-based cameras, to enhance the robustness and generalization of indoor depth completion

To adapt the RDFC-GAN model to leverage additional sensor modalities, such as thermal or event-based cameras, for indoor depth completion, the following strategies can be implemented:

Multi-Modal Fusion: Develop fusion mechanisms that can effectively integrate data from thermal or event-based cameras with RGB and depth information to create a more comprehensive understanding of the indoor scene.

Sensor-Specific Features: Extract sensor-specific features from thermal or event-based camera data and incorporate them into the fusion process to enhance the model's ability to capture unique characteristics captured by these sensors.

Cross-Modal Learning: Implement cross-modal learning techniques to facilitate the transfer of knowledge between different sensor modalities, enabling the model to learn from the strengths of each sensor type and improve depth completion accuracy.

Adaptive Sensor Fusion: Design adaptive sensor fusion modules that can dynamically adjust the weight and importance of different sensor modalities based on the specific characteristics of the indoor environment, ensuring robustness and generalization.

Domain Adaptation: Explore domain adaptation techniques to fine-tune the model on data from different sensor modalities, allowing it to adapt and generalize well to diverse indoor environments with varying sensor inputs.

By incorporating these adaptations, the RDFC-GAN model can effectively leverage additional sensor modalities to enhance the robustness and generalization of indoor depth completion, catering to a wider range of indoor scenarios and sensor configurations.