Single Image Depth Prediction with Wavelet Decomposition (2106.02022v2)

Abstract: We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is integrated in a fully differentiable encoder-decoder architecture. We demonstrate that we can reconstruct high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast with previous works, we show that wavelet coefficients can be learned without direct supervision on coefficients. Instead we supervise only the final depth image that is reconstructed through the inverse wavelet transform. We additionally show that wavelet coefficients can be learned in fully self-supervised scenarios, without access to ground-truth depth. Finally, we apply our method to different state-of-the-art monocular depth estimation models, in each case giving similar or better results compared to the original model, while requiring less than half the multiply-adds in the decoder network. Code at https://github.com/nianticlabs/wavelet-monodepth

• The paper introduces WaveletMonoDepth, which integrates wavelet decomposition with a U-Net-style CNN for efficient monocular depth prediction.
• It leverages Haar wavelets to focus computation on depth discontinuities, reducing multiply-add operations by over 50% compared to traditional methods.
• Experimental results on NYU and KITTI datasets confirm competitive accuracy, supporting real-time applications in autonomous driving and augmented reality.

Overview of "Single Image Depth Prediction with Wavelet Decomposition"

The paper introduces the WaveletMonoDepth technique, which leverages wavelet decomposition to predict depth from single images. This method integrates wavelet transformation with a deep convolutional neural network, presenting an innovative approach to monocular depth estimation. Unlike conventional approaches that focus solely on improving depth prediction accuracy, WaveletMonoDepth emphasizes computational efficiency by embedding wavelet decomposition into the prediction process, thereby conserving resources during inference.

Methodology and Architectures

WaveletMonoDepth employs a U-Net-style architecture with an encoder-decoder setup, integrating wavelet decomposition within the decoder. Here, the network doesn't directly predict wavelet coefficients. Instead, it supervises the final depth image reconstructed through the inverse wavelet transform. This novel approach allows the model to exploit the sparse nature of high-frequency wavelet coefficients, which are primarily non-zero at depth discontinuities commonly present near objects’ edges in images.

The network utilizes Haar wavelets due to their computational simplicity and effectiveness in representing piece-wise smooth regions, which is a prevalent characteristic of man-made environments. The method predicts sparse high-frequency wavelet coefficients alongside a low-resolution depth map, which jointly reconstruct the full-resolution depth image via inverse wavelet transformation. This process intelligently concentrates computational effort on crucial depth discontinuities, minimizing unnecessary operations on flat regions.

Experimental Setup and Results

WaveletMonoDepth was evaluated on the NYU and KITTI datasets, where it demonstrated competitive depth estimation performance with a significant reduction in computation by more than half the multiply-add operations required when compared to state-of-the-art approaches without wavelet incorporation. On the KITTI dataset, various backbone models, such as ResNet18 and ResNet50, were tested, and WaveletMonoDepth achieved accuracy metrics on par with established methods, while offering enhanced efficiency due to the wavelet-induced sparsity.

Moreover, the model was subjected to self-supervised learning scenarios, proving capable of learning depth representations without explicit supervision of the wavelet coefficients themselves. This flexibility is particularly useful for applications lacking dense or accurate ground-truth data.

Implications and Future Directions

The integration of wavelet transformation into depth prediction architecture establishes new avenues for achieving efficient monocular depth estimation, making it feasible for real-time applications in resource-constrained environments such as autonomous driving and augmented reality. Furthermore, WaveletMonoDepth's ability to emphasize computational savings while maintaining high precision paves the way for further exploration into hybrid methods combining traditional signal processing techniques with modern deep learning models.

Future developments could explore the adaptability of other wavelet types or the potential application of similar decompositions in related tasks, such as semantic segmentation or optical flow, where spatial sparsity plays a critical role. Further, enriching this approach with adaptive thresholding strategies for sparsification can refine computation-accuracy tradeoffs dynamically based on input characteristics.

Overall, the paper sets a vital precedent for exploiting mathematical transformations like wavelets in deep learning-based computer vision tasks, underscoring the potential benefits of cross-disciplinary techniques in advancing AI research.