BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation (2204.00987v1)

Abstract: Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention. Recently, some methods reformulate it as a classification-regression task to boost the model performance, where continuous depth is estimated via a linear combination of predicted probability distributions and discrete bins. In this paper, we present a novel framework called BinsFormer, tailored for the classification-regression-based depth estimation. It mainly focuses on two crucial components in the specific task: 1) proper generation of adaptive bins and 2) sufficient interaction between probability distribution and bins predictions. To specify, we employ the Transformer decoder to generate bins, novelly viewing it as a direct set-to-set prediction problem. We further integrate a multi-scale decoder structure to achieve a comprehensive understanding of spatial geometry information and estimate depth maps in a coarse-to-fine manner. Moreover, an extra scene understanding query is proposed to improve the estimation accuracy, which turns out that models can implicitly learn useful information from an auxiliary environment classification task. Extensive experiments on the KITTI, NYU, and SUN RGB-D datasets demonstrate that BinsFormer surpasses state-of-the-art monocular depth estimation methods with prominent margins. Code and pretrained models will be made publicly available at \url{https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox}.

• The paper introduces BinsFormer, reinterpreting depth estimation by combining pixel-level features with Transformer-based adaptive bin generation.
• It leverages a classification-regression paradigm to achieve precise depth maps, significantly outperforming benchmarks like KITTI and NYU-Depth-v2.
• Its multi-scale refinement and auxiliary scene classification enhance performance for practical applications in robotics and autonomous driving.

Insights into "BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation"

The paper introduces "BinsFormer," a novel framework designed to enhance the performance of monocular depth estimation by revisiting the adaptive bins strategy. The approach reinterprets depth estimation as a classification-regression problem, leveraging the strengths of both techniques for enhanced accuracy.

Framework Overview

The BinsFormer framework is built on three core components: a pixel-level module, a Transformer module, and a depth estimation module.

1. Pixel-Level Module: It begins by extracting features from an input image, using a backbone such as SWIN-Transformer or ResNet, generating multi-scale features through an FPN-based decoder to facilitate a fine-grained depth prediction.
2. Transformer Module: This component uses a Transformer decoder to manage adaptive bins generation. By redefining bin generation as a set-to-set prediction problem, the module innovatively harnesses Transformer capabilities to predict bin centers and embeddings. Here, separate queries interact with image features to predict probability distributions for each pixel.
3. Depth Estimation Module: This module synthesizes data from the pixel-level and Transformer modules, estimating depth by linearly combining bin centers with predicted probability distributions.

Additionally, BinsFormer incorporates auxiliary scene classification to improve bin prediction through implicit supervision and employs a multi-scale refinement strategy, which progressively refines depth predictions via hierarchical feature interactions.

Technical Evaluation

The paper demonstrates BinsFormer's superior performance on KITTI, NYU-Depth-v2, and SUN RGB-D datasets, significantly outperforming existing state-of-the-art methods. For instance, on the KITTI dataset, the Swin-Base configuration with ImageNet-22K pre-training achieved notable improvements in metrics such as REL and RMS error. These results underscore the efficacy of integrating Transformer-based adaptive bins and classification-regression tasks.

Implications and Future Directions

The implications of BinsFormer are twofold:

1. Practical: The improved accuracy in depth estimation empowers applications in robotics and autonomous driving, where understanding the spatial layout with a single RGB image is crucial.
2. Theoretical: The paper's approach reframes monocular depth estimation, suggesting avenues for leveraging classification-regression paradigms in other computer vision tasks, potentially sparking further research into Transformer applications for image processing.

The use of Transformers for bin generation is a promising trend, indicating future developments could explore more complex scene understanding tasks or integration with other multi-modal inputs for depth estimation.

In conclusion, BinsFormer represents a substantive contribution to monocular depth estimation, illustrating the potential of adaptive bins and probabilistic methods in harmonizing global and pixel-wise information for improved depth perception.