AdaBins: Depth Estimation using Adaptive Bins (2011.14141v1)

Abstract: We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.

• The paper introduces the AdaBins module that adaptively segments the depth range using a transformer-based approach to enhance single-image depth estimation.
• It refines traditional encoder-decoder networks with a hybrid regression strategy that computes depth as a linear combination of learned bin centers, reducing discretization artifacts.
• The method achieves state-of-the-art performance on datasets like NYU-Depth-v2 and KITTI, offering significant improvements for applications such as autonomous driving and augmented reality.

AdaBins: Depth Estimation using Adaptive Bins

The paper "AdaBins: Depth Estimation using Adaptive Bins" presents a novel approach to depth estimation from single RGB images, leveraging adaptive binning strategies within a transformer-based framework. This research introduces a new architectural component, AdaBins, which enhances traditional convolutional neural network (CNN) architectures by facilitating improved global information processing at high resolutions.

Methodology

The AdaBins architecture improves upon conventional encoder-decoder networks by incorporating a transformer-based module that adaptively segments the depth range into bins per image. This method contrasts with fixed bin approaches by analyzing depth distribution and refining depth predictions through a learned adaptive binning process, providing linear combinations of bin centers as the final output. Such a strategy balances the benefits of classification over a quantized space and the continuous nature of regression.

The architecture consists of several components:

1. Encoder-Decoder Framework: Utilizes EfficientNet B5 as the foundation, which extracts features from input images.
2. AdaBins Module: Integrates a transformer-based mini-ViT block for global attention, computing bin-widths $b$ and Range-Attention-Maps $R$. This facilitates globally aware, adaptive binning, crucial for effective depth estimation.
3. Hybrid Regression Strategy: Offers smoother depth transitions by computing depth as a linear combination of adaptable bin centers rather than selecting the most probable bin, minimizing discretization artifacts.

Contributions and Results

The authors assert AdaBins achieves state-of-the-art results on standard datasets, NYU-Depth-v2 and KITTI, with significant improvements across all metrics. The performance enhancements are attributed to the AdaBins module's global processing capability and dynamic depth distribution adaptation.

Key contributions of the research include:

• Adaptive Binning Strategy: Unlike rigid depth quantization in previous methods, adaptive bins allow the network to focus on plausible depth sub-intervals specific to each input.
• Global Processing at High Resolution: The research demonstrates the efficacy of processing global context earlier in the architecture, avoiding common pitfalls of low-resolution bottleneck representations.
• Improvements in Depth Estimation Metrics: Demonstrates significant gains over competitive architectures like BTS and DAV, particularly in accuracy and error reduction metrics.

Implications and Future Directions

The implications of this research are multi-dimensional. Practically, it offers advancements in applications requiring precise spatial understanding, such as autonomous driving and augmented reality. Theoretically, it suggests promising directions in combining CNNs with emerging transformer architectures, advocating for adaptive strategies in depth-related tasks.

Future research directions may delve into extending adaptive bin strategies to other vision tasks, exploring multi-task learning paradigms, or refining transformer integration for efficiently capturing spatial-semantic relationships.

The work presented in "AdaBins: Depth Estimation using Adaptive Bins" distinctly moves the needle in depth estimation, offering a robust framework that other computational imaging tasks may benefit from adapting.