From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation (1907.10326v6)

Abstract: Estimating accurate depth from a single image is challenging because it is an ill-posed problem as infinitely many 3D scenes can be projected to the same 2D scene. However, recent works based on deep convolutional neural networks show great progress with plausible results. The convolutional neural networks are generally composed of two parts: an encoder for dense feature extraction and a decoder for predicting the desired depth. In the encoder-decoder schemes, repeated strided convolution and spatial pooling layers lower the spatial resolution of transitional outputs, and several techniques such as skip connections or multi-layer deconvolutional networks are adopted to recover the original resolution for effective dense prediction. In this paper, for more effective guidance of densely encoded features to the desired depth prediction, we propose a network architecture that utilizes novel local planar guidance layers located at multiple stages in the decoding phase. We show that the proposed method outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks. We also provide results from an ablation study to validate the effectiveness of the proposed method.

• The paper proposes a novel local planar guidance technique integrated in decoders to improve depth reconstruction accuracy.
• It employs an encoder-decoder CNN with advanced backbones and an ASPP module for robust feature and context extraction.
• Experimental results on NYU Depth V2 and KITTI datasets demonstrate state-of-the-art performance and enhanced object boundary precision.

Monocular Depth Estimation via Multi-Scale Local Planar Guidance

The paper "From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation" presents a novel approach to the persistent challenge of depth estimation from a single 2D image. This task is inherently ill-posed due to the potential for numerous 3D scenes to correspond to identical 2D projections. The authors propose a network architecture that incorporates innovative local planar guidance (LPG) layers to markedly enhance reconstruction accuracy across multiple scales during the decoding phase of convolutional neural networks (CNNs).

Methodology and Implementation

The authors leverage an encoder-decoder structure typical in deep convolutional neural networks. The encoder is responsible for dense feature extraction, while the decoder predicts depth. This paper introduces LPG layers at various stages in the decoding process, facilitating effective guidance from dense features to precise depth prediction. The LPG layers allow for explicit relations to be defined between intermediate features and the final output, employing a local planar assumption.

Key Components:

• Dense Feature Extractor: Uses pre-trained networks like ResNet, DenseNet, and ResNext as backbones.
• ASPP Module: An atrous spatial pyramid pooling module is employed for context extraction.
• Local Planar Guidance Layers: Located at 1/8, 1/4, and 1/2 spatial resolutions, these layers learn to map regional features to full-resolution depth predictions by estimating 4-dimensional plane coefficients.

Through this architecture, the network synthesizes feature maps into depth estimates with enhanced detail, aided by the use of the local planar assumption which reduces complexity by guiding local depth cue generation.

Experimental Results

The proposed method demonstrates superior performance across several benchmarks, notably the NYU Depth V2 and KITTI datasets. The architecture achieves state-of-the-art results, outperforming previous approaches in terms of both accuracy and computational efficiency. Specifically, on the NYU Depth V2 dataset, the DenseNet-161 based model yields exceptional improvement over prior work, evidenced by metrics such as reduced RMSE and increased delta threshold accuracy.

Evaluation:

• State-of-the-Art Performance: Exhibits significant improvement over existing methods in depth estimation accuracy on challenging datasets.
• Higher Precision in Object Boundaries: Illustrated through qualitative results, the method provides clearer delineation of object boundaries compared to competitors.
• Robust Across Encoder Variants: Efficiently adapts to different backbone networks, showing consistent performance gains.

Implications and Future Prospects

The introduction of the LPG mechanism sets a promising direction for enhancing depth estimation capabilities within CNN frameworks. The ability to efficiently map local features onto a coherent depth representation addresses core limitations faced by previous methods. This approach could inform further research aimed at improving detail restoration and generalization across diverse environments, including scenarios with sparse ground truth data.

Future work might explore the integration of photometric reconstruction losses to counteract limitations posed by sparse datasets like KITTI, potentially enhancing the network's ability to generalize with less supervision. Additionally, extending the approach to unsupervised or semi-supervised paradigms could broaden its applicability across various computer vision applications, particularly in robotics and autonomous systems.