Single-Image Depth Perception in the Wild
Proposes a deep network with relative depth annotations and a new wild scene dataset, significantly improving single-image depth estimation.
Key Findings
Methodology
This paper introduces an end-to-end deep depth regression network based on a multi-scale Hourglass architecture, trained with a novel relative depth loss. The dataset 'Depth in the Wild' (DIW) was crowdsourced, annotating only one pair of points per image to maximize efficiency. The model employs a ranking loss to enforce depth ordering, avoiding reliance on absolute depth labels. Combining existing RGB-D data with relative annotations, the approach enhances depth perception in unconstrained environments. The network directly predicts pixel-wise depth, simplifying previous complex optimization procedures.
Key Results
- On NYU Depth, our method achieves a WKDR of 28.6%, outperforming Zoran et al.'s 43.5%. The RMSE reaches 1.10, better than prior methods. On the DIW dataset, the model reduces WHDR to 14.39%, significantly better than Eigen et al.'s 25.70%. Combining RGB-D and relative depth annotations yields substantial performance gains, demonstrating the effectiveness of data fusion.
- Training with randomly sampled point pairs, our model performs comparably to superpixel-based sampling, indicating robustness and generalization. The multi-scale Hourglass architecture captures features at different resolutions, and the ranking loss guides pixel-level depth prediction, streamlining the process compared to traditional energy minimization.
- Extensive outdoor scene testing shows the model's robustness, especially in complex environments, outperforming models trained solely on RGB-D data. Results confirm that crowdsourced relative depth annotations can effectively scale up training data for wild scene depth estimation.
Significance
This work addresses the scarcity of large-scale outdoor depth datasets by leveraging crowdsourcing of relative depth labels, enabling training on diverse, unconstrained images. The approach reduces dependence on costly depth sensors, broadening the applicability of depth estimation to real-world scenarios such as autonomous driving and robotics. It bridges the gap between indoor RGB-D datasets and the need for outdoor scene understanding, marking a significant step toward scalable, practical depth perception systems.
Technical Contribution
The paper's core innovation is an end-to-end pixel-wise depth regression network trained with a ranking loss on relative depth labels. It departs from prior methods that rely on superpixel relations and energy optimization, offering a simpler, more scalable solution. The newly introduced DIW dataset provides a large, diverse training resource. The combination of multi-scale feature extraction and ranking loss ensures accurate depth ordering and pixel-level depth prediction, advancing the state of the art in wild scene depth estimation.
Novelty
This is the first large-scale dataset of wild scene images annotated with relative depth, enabling training of deep models without absolute depth labels. The proposed model directly predicts pixel-wise depth using a ranking loss, avoiding complex relation reconciliation. This end-to-end approach simplifies the pipeline and improves performance over previous relation-based methods like Zoran et al., establishing a new benchmark for outdoor depth estimation.
Limitations
- The model struggles with transparent or highly reflective surfaces due to limited training examples of such scenarios, leading to inaccuracies in these regions.
- Crowdsourced annotations, while efficient, can introduce subjective biases, especially in ambiguous cases, affecting data quality.
- Absolute depth estimation remains imprecise, as the model primarily learns depth orderings; integrating scale cues could further improve metric accuracy.
Future Work
Future directions include integrating semantic and motion cues, expanding the dataset with more scene types, and exploring unsupervised or semi-supervised learning to reduce annotation reliance. Enhancing the model's ability to estimate absolute depth and handle challenging surfaces will be key. Additionally, deploying the approach in real-time systems for robotics and autonomous vehicles is a promising avenue.
AI Executive Summary
Single-image depth perception in unconstrained environments remains a formidable challenge due to the scarcity of large, diverse datasets and the complexity of scene structures. Traditional methods rely heavily on RGB-D sensors or limited indoor datasets, which do not generalize well to outdoor or wild scenes. Recognizing this gap, the authors introduce a novel approach that leverages crowdsourced relative depth annotations to scale up training data for wild scene depth estimation.
The core innovation is a deep neural network based on a multi-scale Hourglass architecture, trained with a ranking loss that encourages pixel-wise depth ordering. This design simplifies the pipeline by directly regressing depth from images, bypassing the need for complex relation reconciliation or superpixel-based optimization. The dataset 'Depth in the Wild' (DIW), comprising 495,000 images with annotated point pairs, provides a rich resource for training and evaluation. Each annotation involves only one pair per image, maximizing annotation efficiency.
Experimental results demonstrate the effectiveness of the approach. On indoor datasets like NYU Depth, the model outperforms previous state-of-the-art methods, achieving a WKDR of 28.6% and RMSE of 1.10. More impressively, on the new DIW dataset, the model reduces the Weighted Human Disagreement Rate (WHDR) to 14.39%, significantly better than prior methods. Combining RGB-D data with relative annotations yields further improvements, especially in outdoor scenes, illustrating the method's scalability and robustness.
This work marks a significant step toward practical, scalable depth estimation in the wild. It opens avenues for deploying depth perception in autonomous driving, robotics, and augmented reality, where diverse and unconstrained environments are the norm. Despite some limitations in transparent or reflective surfaces, the approach's simplicity and effectiveness suggest a promising future for large-scale, annotation-efficient depth learning in complex real-world scenarios.
Deep Dive
Abstract
This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.