Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks (1605.02305v3)

Abstract: Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently. In this article, we take advantage of the recent deep residual networks and propose a simple yet effective approach to this problem. We formulate depth estimation as a pixel-wise classification task. Specifically, we first discretize the continuous depth values into multiple bins and label the bins according to their depth range. Then we train fully convolutional deep residual networks to predict the depth label of each pixel. Performing discrete depth label classification instead of continuous depth value regression allows us to predict a confidence in the form of probability distribution. We further apply fully-connected conditional random fields (CRF) as a post processing step to enforce local smoothness interactions, which improves the results. We evaluate our approach on both indoor and outdoor datasets and achieve state-of-the-art performance.

• The paper introduces a paradigm shift by recasting depth estimation as a pixel-wise classification task using discretized depth bins.
• It leverages deep fully convolutional Residual Networks (e.g., ResNet101/152) and conditional random fields to integrate spatial context and refine predictions.
• Experimental results on NYUD2 and KITTI demonstrate state-of-the-art accuracy and robust generalization under challenging conditions.

Estimating Depth from Monocular Images: A Classification Approach with Deep Fully Convolutional Residual Networks

The paper presents a novel approach for depth estimation from single monocular images. The research navigates the traditional challenge in computer vision of predicting depth, which has predominantly been addressed through regression owing to the continuous nature of depth values. The authors propose an innovative shift from regression to a classification paradigm by discretizing depth values into discrete bins and subsequently treating depth estimation as a pixel-wise classification task.

Methodology Overview

The core idea is straightforward yet impactful. By translating the estimation problem into a classification framework, the authors leverage the power of deep fully convolutional residual networks (ResNets). The network learns to classify each pixel into a depth range rather than outputting precise depth values directly. This technique bypasses the challenge of exact regression, which can be prone to inaccuracies due to the inherent complexity and noise in depth data.

Depth Discretization

The continuous depth information from images is uniformly discretized into bins within the logarithmic space. This transformation allows the depth prediction task to harness classification probabilities, which in turn aids in gauging prediction confidence. The application of an information gain loss leverages predictions close to the ground truth, thus refining the training process by focusing more accurately on meaningful data.

Network Architecture

The paper implements a deep residual network architecture, notably using ResNet variants with 101 and 152 layers. These architectures are adapted for dense prediction tasks by replacing fully connected layers with convolutional ones, allowing for the processing of images of varied sizes. The network outputs are post-processed using fully-connected conditional random fields (CRFs), which help in refining the classification output by integrating spatial smoothness and contextual information.

Experimental Results

The authors validate their approach on widely recognized RGB-D benchmark datasets, namely NYUD2 for indoor scenes and KITTI for outdoor scenarios. The results demonstrate state-of-the-art performance, especially in scenarios posing substantial challenges due to various object scales and occlusions. The classification approach provides not only competitive accuracy but also robust prediction confidence, which is effectively leveraged by CRFs for post-processing enhancements.

Comparative Performance

The proposed approach significantly outperforms existing models that rely on regression frameworks. For instance, the method achieves superior accuracy across multiple metrics such as root mean squared error (rms), average relative error (rel), and accuracy under threshold constraints. Particularly, the method shines under challenging conditions, indicating robust generalization and adaptability across different environments.

Implications and Future Work

This reformation from regression to classification for depth estimation could be a critical inflection for further research in computer vision. By harnessing a classification framework, this paper opens avenues for better integration with semantic segmentation and object recognition tasks, which similarly benefit from discrete classification models.

Future work could explore dimensions such as multi-scale inputs to capture varying contextual information, integrating mid-layer features to enrich model understanding, or adopting more efficient up-sampling schemes to enhance prediction resolution. Additionally, this framework could be adapted for real-time applications and mobile environments where computational efficiency and speed are paramount.

In conclusion, the method pioneered by this research demonstrates a robust alternative to conventional depth estimation techniques, emphasizing the potential of classification strategies in deep learning architectures. As the field advances, the incorporation of such methods is likely to evolve, driving further innovations in scene understanding and 3D reconstruction from monocular images.