Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image (1709.07492v2)

Abstract: We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a higher level of robustness and accuracy, we introduce additional sparse depth samples, which are either acquired with a low-resolution depth sensor or computed via visual Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on prediction accuracy. Our experiments show that, compared to using only RGB images, the addition of 100 spatially random depth samples reduces the prediction root-mean-square error by 50% on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of reliable prediction from 59% to 92% on the KITTI dataset. We demonstrate two applications of the proposed algorithm: a plug-in module in SLAM to convert sparse maps to dense maps, and super-resolution for LiDARs. Software and video demonstration are publicly available.

• The paper’s main contribution is a deep regression network that fuses sparse depth samples with a single RGB image for robust and accurate depth prediction.
• Using just 100 sparse samples, the approach reduces RMSE by 50% on NYU-Depth-v2 and improves KITTI’s reliable predictions from 59% to 92%.
• The network’s encoder-decoder design with a ResNet backbone effectively integrates heterogeneous input data, offering promising applications in SLAM and autonomous navigation.

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

The paper presents an innovative method for dense depth prediction by integrating information from both sparse depth samples and a single RGB image. Recognizing the inherent challenges and uncertainties in depth estimation purely from monocular images, the authors' approach leverages additional sparse depth data, obtained via low-resolution depth sensors or visual SLAM algorithms, to enhance both robustness and accuracy of depth predictions.

The primary contribution is a deep regression network capable of learning from raw RGB-D data. The network's architecture is tailored to effectively integrate varying modalities of input data and to handle different dataset sizes and image dimensions, specifically designed for challenging datasets like NYU-Depth-v2 and KITTI.

Numerical Results and Methodology

• NYU-Depth-v2 Improvements: Incorporating 100 sparse depth samples yields a remarkable 50% reduction in root mean square error (RMSE) when compared to RGB-only predictions. This substantial improvement underscores the efficacy of utilizing additional sparse depth information in image-based depth estimation tasks.
• KITTI Benchmark Performance: The approach demonstrates significant enhancement in reliability, with the percentage of reliable predictions rising from 59% to 92% upon integration of sparse depth data. This result further illustrates the method's robustness in real-world, outdoor environments, which are inherently more complex and variable than indoor scenarios.
• Network Architecture: The network employs an encoder-decoder structure with ResNet as the backbone for feature extraction. The choice of up-sampling techniques, data augmentation strategies, and appropriate loss functions are empirically validated to maximize prediction accuracy.

Strong Claims and Implications

The paper claims a significant improvement in prediction accuracy by integrating sparse depth data, noting that even a small number of samples drastically improves performance. It proposes two potential applications: enhancing SLAM systems by converting sparse maps to dense and improving LiDAR resolution via super-resolution techniques.

Implications span both practical and theoretical domains. Practically, the methodology offers cost-effective depth estimation solutions for industries reliant on accurate 3D mapping and navigation, such as autonomous vehicles and robotics. Theoretically, the paper opens discussion on modality fusion in neural networks, inviting further exploration into how hybrid models can optimize learning from disparate data sources.

Future Directions

The research suggests potential avenues for future work, particularly in refining network architectures to better learn from combined RGB and sparse depth data. Exploring alternative feature extraction techniques and incorporating temporal data from video sequences could enhance prediction reliability further. Additionally, extending the application domain beyond automotive and SLAM systems could uncover new opportunities in augmented reality or virtual reality environments.

In summary, this paper presents a robust approach to bridging the gap between sparse depth information and the need for high-fidelity depth maps. Its integration of deep learning techniques with sparse sensor data provides a compelling solution to one of 3D vision's persistent challenges.