Elite360D: Efficient 360 Depth Estimation Framework

Khái niệm cốt lõi

Elite360D proposes a novel framework for efficient 360 depth estimation by leveraging ERP and ICOSAP projections, outperforming prior methods on benchmark datasets.

Tóm tắt

Elite360D introduces a novel framework for 360 depth estimation.
The framework includes an ERP image encoder, ICOSAP point encoder, and B2F module.
B2F module captures semantic- and distance-aware dependencies between ERP and ICOSAP features.
Extensive experiments show Elite360D's superiority over existing methods on various datasets.

Thống kê

Elite360D significantly improves plain-backbones' performance with minimal computational memory (only about 1M parameters).
Our Elite360D with ResNet-34 outperforms UniFuse by 2.53% (Abs Rel) on the Matterport3D dataset.

Trích dẫn

"Our main contributions are three-fold: introducing ICOSAP, proposing the B2F module, and supporting diverse off-the-shelf models." - Elite360D Paper

Thông tin chi tiết chính được chắt lọc từ

Elite360D

by Hao Ai,Lin W... lúc arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16376.pdf

Yêu cầu sâu hơn

How does the use of ICOSAP improve global perception in depth estimation

The use of ICOSAP improves global perception in depth estimation by providing a spatially continuous and globally perceptive non-Euclidean projection for 360° images. Unlike other projections like CP or TP, which are spatially discontinuous and require complex re-projection operations, ICOSAP maintains spatial information without redundancy. This allows each ERP pixel-wise feature with limited local receptive fields to capture the entire scene more effectively. By representing ICOSAP as discrete points and leveraging its global awareness, the model can learn representations from a local-with-global perspective, enhancing the understanding of large FoV scenes.

What are the implications of reducing computational costs while maintaining spatial information

Reducing computational costs while maintaining spatial information has significant implications for efficiency and performance in depth estimation tasks. By using ICOSAP as a point set representation instead of more computationally intensive methods like unfolded mesh representations or spherical polyhedron representations, Elite360D achieves a balance between accuracy and efficiency. The reduction in computational costs means faster processing times, lower resource requirements, and potentially wider applicability across different platforms or devices. Maintaining spatial information ensures that the model retains crucial details about the scene while optimizing resource utilization.

How can the B2F module be adapted for other applications beyond depth estimation

The B2F module can be adapted for various applications beyond depth estimation that require semantic- and distance-aware feature fusion. One potential application is semantic segmentation in omnidirectional imagery where capturing both local details and global context is essential for accurate labeling of objects within a scene. By modifying the attention mechanisms within the B2F module to focus on different types of features (e.g., textures, colors), it can enhance segmentation results by considering both semantic similarities between pixels/features as well as their spatial relationships.
Another application could be object detection in panoramic images where understanding long-range dependencies between objects is crucial for accurate localization and classification. The B2F module's ability to capture semantic- and distance-aware dependencies makes it suitable for improving object detection performance by enabling better integration of contextual information across wide field-of-view scenes.
Overall, adapting the B2F module for these applications would leverage its strengths in modeling complex relationships between features from different perspectives or projections to enhance various computer vision tasks beyond depth estimation.