Depth map super-resolution using stereo-vision-assisted model

Depth maps captured by consumer-level depth cameras such as Kinect are usually degraded by noise, missing values, and quantization. In this paper, we present a data-driven approach for refining degraded RAWdepth maps that are coupled with an RGB image. The key idea of our approach is to take advantage of a training set of high-quality depth data and transfer its information to the RAW depth map through multi-scale dictionary learning. Utilizing a sparse representation, our method learns a dictionary of geometric primitives which captures the correlation between high-quality mesh data, RAW depth maps and RGB images. The dictionary is learned and applied in a manner that accounts for various practical issues that arise in dictionary-based depth refinement. Compared to previous approaches that only utilize the correlation between RAW depth maps and RGB images, our method produces improved depth maps without over-smoothing. Since our approach is data driven, the refinement can be targeted to a specific class of objects by employing a corresponding training set. In our experiments, we show that this leads to additional improvements in recovering depth maps of human faces.

This paper presents a method for increasing spatial resolution of a depth map using its corresponding high-resolution (HR) color image as a guide. Most of the previous methods rely on the assumption that depth discontinuities are highly correlated with color boundaries, leading to artifacts in the regions where the assumption is broken. To prevent scene texture from being erroneously transferred to reconstructed scene surfaces, we propose a framework for dividing the color image into different regions and applying different methods tailored to each region type. For the region classification, we first segment the low-resolution (LR) depth map into regions of smooth surfaces, and then use them to guide the segmentation of the color image. Using the consensus of multiple image segmentations obtained by different super-pixel generation methods, the color image is divided into continuous and discontinuous regions: in the continuous regions, their HR depth values are interpolated from LR depth samples without exploiting the color information. In the discontinuous regions, their HR depth values are estimated by sequentially applying more complicated depth-histogram-based methods. Through experiments, we show that each step of our method improves depth map upsampling both quantitatively and qualitatively. We also show that our method can be extended to handle real data with occluded regions caused by the displacement between color and depth sensors.

In this paper, we propose a simple and effective depth upsampling technique using self-guided residual interpolation. The original residual interpolation requires guidance information such as high-resolution RGB color image. However, self-guided residual interpolation requires only a single depth map. In the proposed algorithm, a tentative estimation of a high-resolution depth map is first generated from an input low-resolution depth map. Then, re-interpolation is applied to the residual domain, which is defined by differences between the input depth map and the tentative estimate. A precise high-resolution depth map is obtainable by interpolating in the residual domain. Experimental results demonstrate that our algorithm can outperform state-of-the-art depth map upsampling algorithms.

A depth map upsampling method using depth local features is proposed. Depth discontinuity and depth variance are extracted from a low-resolution depth map and a colour image as the depth local features. They are incorporated into an energy function for the Markov random field (MRF)-based depth upsampling. The high-resolution depth map is obtained by optimising the energy function using belief propagation. The experimental results show that the proposed method outperforms other depth upsampling approaches in terms of the bad pixel rate.

The depth maps captured by RGB-D cameras usually are of low resolution, entailing recent efforts to develop depth super-resolution (DSR) methods. However, several problems remain in existing DSR methods. First, conventional DSR methods often suffer from unexpected artifacts. Secondly, high-resolution (HR) RGB features and low-resolution (LR) depth features are often fused in shallow layers only. Thirdly, only the last layer of features is used for reconstruction. To address the above problems, we propose Coupled U-Net (CU-Net), a new color image guided DSR method built on two U-Net branches for HR color images and LR depth maps, respectively. The CU-Net embeds a dual skip connection structure to leverage the feature interaction of the two branches, and a multi-scale fusion to fuse the deeper and multi-scale features of two branch decoders for more effective feature reconstruction. Moreover, a channel attention module is proposed to eliminate artifacts. Extensive experiments show that the proposed CU-Net outperforms state-of-the-art methods.

This paper presents a novel method for depth map enhancement. Given a low resolution and noisy depth map, we improve its spatial resolution and decrease noise with a registered high resolution color image. Different from the previous window-based local filter methods, this approach is based on geodesic distance weighted nonlocal filter, which does not require the optimal support window and more natural pixel similarity measure metric is given. By quantitative and qualitative experiments on publicly available test sequences, we demonstrate the effectiveness and efficiency of our proposed method on the depth enhancement task, compared with the existing methods.

Depth image super-resolution based on joint sparse coding

In this paper, we propose an efficient up-sampling method of the low-resolution depth map that is obtained by a time-of-flight (TOF) depth sensor. After we capture the color images and TOF depth maps simultaneously using a fusion camera system, each pixel of the low-resolution depth map is relocated to the corresponding color image positions by 3D warping, and the warping error is eliminated in each color segment. Then, we employ a Markov random field (MRF) model to produce a high-resolution disparity map using the warped values and color segment information. Experimental results show that the proposed method efficiently up-sample the low-resolution depth maps compared to the other up-sampling approaches.

Consumer depth sensors such as time-of-flight camera or Kinect have gained significant popularity in recently. However, the captured depth maps suffer from limited spatial resolution and a variety of noise, making such depth maps difficult to be directly applied in related applications. In this paper, we present a novel single depth map super-resolution method, aiming to reconstruct high-resolution depth map from its associated low-resolution depth map without any auxiliary information. Particularly, we exploit the depth local self-similarity to assist in constructing patch pairs in terms of high-resolution and low-resolution depth edge patches, and then deduce a high-resolution depth edge map via Markov model. Finally, we implement a joint bilateral filter to reconstruct the high-resolution depth map. Experimental results show that our method overcomes existing methods on the benchmark database as well as Kinect captured depth maps.

The paper presents a depth map super-resolution method of which the core content is a novel edge enhancement algorithm. Auto-regressive algorithm is applied to generate an initial upsampled depth map before the edge enhancement. Except for the low-resolution depth map, an intensity image derived from high-resolution color image is also utilized to extract accurate depth edge, which is finally rectified by combining color, depth and intensity information. The experimental results show that our approach is able to recover high-resolution (HR) depth maps with high quality. What’s more, in comparison with the previous state-of-art algorithms, our approach can generally achieve better results.

The area of computer vision is one of the most discussed topics amongst many scholars, and stereo matching is its most important sub fields. After the parallax map is transformed into a depth map, it can be applied to many intelligent fields. In this paper, a stereo matching algorithm based on visual sensitive information is proposed by using standard images from Middlebury dataset. Aiming at the limitation of traditional stereo matching algorithms regarding the cost window, a cost aggregation algorithm based on the dynamic window is proposed, and the disparity image is optimized by using left and right consistency detection to further reduce the error matching rate. The experimental results show that the proposed algorithm can effectively enhance the stereo matching effect of the image providing significant improvement in accuracy as compared with the classical census algorithm. The proposed model code, dataset, and experimental results are available at https://github.com/WangHewei16/Stereo-Matching.

Limited spatial resolution and varieties of degradations are the main restrictions of today’s captured depth map by active 3D sensing devices. Typical restrictions limit the direct use of the obtained depth maps in most of 3D applications. In this paper, we present a single depth map upsampling approach in contrast to the common work of using the corresponding combined color image to guide the upsampling process. The proposed approach employs a multi-level decomposition to convert the depth upsampling process to a classification-based problem via a multi-level classification-based learning algorithm. Hence, the lost high frequency details can be better preserved at different levels. The adopted multi-level decomposition algorithm utilizes $$l_{1} ,$$ and $$l_{0}$$ sparse regularization with total-variation regularization to keep structure- and edge-preserving smoothing with robustness to noisy degradations. In addition, the proposed classification-based learning algorithm supports the accuracy of discrimination by learning discriminative dictionaries that carry original features about each class and learning common shared dictionaries that represent the shared features between classes. The proposed algorithm has been validated via different experiments under variety of degradations using different datasets from different sensing devices. Results show superiority to the state of the art, especially in case of upsampling noisy low-resolution depth maps.

Depth information is being widely used in many real-world applications. However, due to the limitation of depth sensing technology, the captured depth map in practice usually has much lower resolution than that of color image counterpart. In this paper, we propose to joint exploit the internal smoothness prior and external gradient consistency constraint in graph domain for depth super-resolution. On one hand, a new graph Laplacian regularizer is proposed to the preserve the inherent piecewise smooth characteristic of depth, which has desirable filtering properties. On the other hand, inspired by an observation that the gradient of depth is zero except at edge separating regions, we introduce a graph gradient consistency constraint to enforce that the graph gradient of depth is close to the thresholded gradient of guidance. Finally, the internal and external regularizations are casted into a unified optimization framework, which can be efficiently addressed by ADMM. Experiments results demonstrate that our method outperforms the state-of-the-art with respect to both objective and subjective quality evaluations.

In this paper, we present a novel method to upsample the depth map obtained by the Time-of-Flight (ToF) camera with the guidance of the companion high resolution color image. The problem is modeled with an optimization framework where we use a novel exponential function as the error norm. By using this novel error norm, our model could take the properties of the depth map itself into account. Depth discontinuity cues are obtained not only from the color image but also the depth map itself. To further enhance the performance, we perform a data driven selection of the parameter in the model to better fit the property of the depth map. Experimental results show that our method has excellent performance in smoothing the noise, preserving sharp depth discontinuities and suppressing the texture copy effect.

Depth map super-resolution is a task with high practical application requirements in the industry. Existing color-guided depth map super-resolution methods usually necessitate an extra branch to extract high-frequency detail information from RGB image to guide the low-resolution depth map reconstruction. However, because there are still some differences between the two modalities, direct information transmission in the feature dimension or edge map dimension cannot achieve satisfactory result, and may even trigger texture copying in areas where the structures of the RGB-D pair are inconsistent. Inspired by the multi-task learning, we propose a joint learning network of depth map super-resolution (DSR) and monocular depth estimation (MDE) without introducing additional supervision labels. For the interaction of two subnetworks, we adopt a differentiated guidance strategy and design two bridges correspondingly. One is the high-frequency attention bridge (HABdg) designed for the feature encoding process, which learns the high-frequency information of the MDE task to guide the DSR task. The other is the content guidance bridge (CGBdg) designed for the depth map reconstruction process, which provides the content guidance learned from DSR task for MDE task. The entire network architecture is highly portable and can provide a paradigm for associating the DSR and MDE tasks. Extensive experiments on benchmark datasets demonstrate that our method achieves competitive performance. Our code and models are available at https://rmcong.github.io/proj_BridgeNet.html.