ECoDepth: Conditioning Diffusion Models for Monocular Depth Estimation

Kernekoncepter

Using ViT embeddings improves monocular depth estimation.

Resumé

Abstract

Learning-based single image depth estimation relies on shading and contextual cues.
ViT embeddings provide detailed contextual information for depth estimation.
Proposed model achieves state-of-the-art performance on NYU Depth v2 and KITTI datasets.

Introduction

Single Image Depth Estimation (SIDE) is crucial for various applications.
Metric and relative depth estimation techniques are used.
Learning-based models rely on visual cues for depth prediction.

Data-Driven Approach

Models overfit on specific training data distributions.
Training on multiple datasets with varied depth ranges is proposed.

Foundational Models

Pre-trained models like ViT improve generalization and zero-shot transfer.
Comparison with existing works like VPD and TADP.

Proposed Methodology

CIDE module extracts semantic context using ViT embeddings.
Conditional diffusion model for depth prediction.

Experiments and Results

Evaluation on NYU Depth v2 and KITTI datasets.
Generalization and zero-shot transfer performance.
Ablation study on the effectiveness of contextual information.

Conclusion

CIDE module enhances monocular depth estimation.
ViT embeddings outperform text embeddings for depth prediction.

Statistik

Achieving Abs Rel error of 0.059 (14% improvement) on NYU Depth v2 dataset.
Achieving Sq Rel error of 0.139 (2% improvement) on KITTI dataset.
Mean relative improvement of (20%, 23%, 81%, 25%) over NeWCRF on (Sun-RGBD, iBims1, DIODE, HyperSim) datasets.

Citater

"ViT embeddings provide more relevant information for depth estimation than pseudo-captions."
"Our model outperforms existing approaches on benchmark datasets."

Vigtigste indsigter udtrukket fra

ECoDepth

by Suraj Patni,... kl. arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18807.pdf

Dybere Forespørgsler

How can the proposed CIDE module be adapted for other computer vision tasks?

The Comprehensive Image Detail Embedding (CIDE) module proposed in the context can be adapted for other computer vision tasks by leveraging the rich semantic information extracted from ViT embeddings. Here are some ways to adapt the CIDE module for different tasks:

Object Detection: The ViT embeddings can be used to provide detailed contextual information for object detection tasks. By conditioning the detection model on these embeddings, it can better understand the relationships between objects in an image.
Semantic Segmentation: The CIDE module can be integrated into semantic segmentation models to improve the understanding of object boundaries and categories. The ViT embeddings can help in capturing fine-grained details for accurate segmentation.
Image Captioning: For image captioning tasks, the ViT embeddings can enhance the contextual understanding of the image content. By conditioning the captioning model on these embeddings, it can generate more descriptive and accurate captions.
Visual Question Answering (VQA): In VQA tasks, the CIDE module can provide additional context for answering questions about images. The ViT embeddings can help in identifying relevant visual information to support the answers.

How can the model's performance be enhanced further for outdoor depth estimation scenarios?

To enhance the model's performance for outdoor depth estimation scenarios, several strategies can be implemented:

Data Augmentation: Incorporate outdoor-specific data augmentation techniques such as simulating different weather conditions, lighting variations, and occlusions commonly found in outdoor scenes.
Fine-tuning on Outdoor Datasets: Fine-tune the model on outdoor-specific datasets like Cityscapes or Waymo Open Dataset to adapt it to the challenges posed by outdoor environments.
Multi-Modal Fusion: Integrate additional sensor modalities like LiDAR or radar data to provide complementary depth information and improve accuracy in outdoor settings.
Domain Adaptation: Implement domain adaptation techniques to bridge the gap between indoor and outdoor scenes, ensuring the model generalizes well to diverse environments.
Architectural Enhancements: Explore advanced architectures or ensemble models that are specifically designed to handle the complexities of outdoor depth estimation tasks.

What challenges may arise when implementing ViT embeddings in real-time applications?

Implementing ViT embeddings in real-time applications may pose the following challenges:

Computational Complexity: ViT models are computationally intensive, requiring significant resources for inference in real-time applications, which can lead to latency issues.
Memory Constraints: Storing and processing large ViT embeddings in real-time may strain memory resources, especially on devices with limited memory capacity.
Model Size: ViT models have a large number of parameters, leading to larger model sizes, which can be challenging to deploy on resource-constrained devices.
Inference Speed: Generating ViT embeddings for each input image in real-time may impact the overall inference speed, potentially causing delays in processing.
Optimization: Optimizing ViT models for real-time inference while maintaining accuracy can be a complex task, requiring efficient implementation and tuning of hyperparameters.

By addressing these challenges through optimization techniques, model compression, and hardware acceleration, the integration of ViT embeddings in real-time applications can be made more feasible.