3DV quality model based depth maps for view synthesis in FTV system

Depth maps are used for synthesis virtual view in free-viewpoint television （FTV） systems. When depth maps are derived using existing depth estimation methods, the depth distortions will cause undesirable artifacts in the synthesized views. To solve this problem, a 3D video quality model base depth maps （D-3DV） for virtual view synthesis and depth map coding in the FTV applications is proposed. First, the relationships between distortions in coded depth map and rendered view are derived. Then, a precisely 3DV quality model based depth characteristics is develop for the synthesized virtual views. Finally, based on D-3DV model, a multilateral filtering is applied as a pre-processed filter to reduce rendering artifacts. The experimental results evaluated by objective and subjective methods indicate that the proposed D-3DV model can reduce bit-rate of depth coding and achieve better rendering quality.

Technique of Embedding Depth Maps into 2D Images

This paper proposes a new technique that is used to embed depth maps into corresponding 2-dimensional （2D） images. Since a 2D image and its depth map are integrated into one type of image format, they can be treated as if they were one 2D image. Thereby, it can reduce the amount of data in 3D images by half and simplify the processes for sending them through networks because the synchronization between images for the left and right eyes becomes unnecessary. We embed depth maps in the quantized discrete cosine transform （DCT） data of 2D images. The key to this technique is whether the depth maps could be embedded into 2D images without perceivably deteriorating their quality. We try to reduce their deterioration by compressing the depth map data by using the differences from the next pixel to the left. We assume that there is only one non-zero pixel at most on one horizontal line in the DCT block because the depth map values change abruptly. We conduct an experiment to evaluate the quality of the 2D images embedded with depth maps and find that satisfactory quality could be achieved.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

Consistent Depth Maps Estimation from Binocular Stereo Video Sequence

In the paper, an approach is proposed for the problem of consistency in depth maps estimation from binocular stereo video sequence. The consistent method includes temporal consistency and spatial consistency to eliminate the flickering artifacts and smooth inaccuracy in depth recovery. So the improved global stereo matching based on graph cut and energy optimization is implemented. In temporal domain, the penalty function with coherence factor is introduced for temporal consistency, and the factor is determined by Lucas-Kanade optical flow weighted histogram similarity constraint(LKWHSC). In spatial domain, the joint bilateral truncated absolute difference(JBTAD) is proposed for segmentation smoothing. The method can smooth naturally and uniformly in low-gradient region and avoid over-smoothing as well as keep edge sharpness in high-gradient discontinuities to realize spatial consistency. The experimental results show that the algorithm can obtain better spatial and temporal consistent depth maps compared with the existing algorithms.

Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps.This work uses the consistency check method to find an accurate depth map for identifying occluded pixels.The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation.The improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction algorithms.The experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and runtime.We observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain cumbersome.Considering this gain,we have created our dataset with occlu-sion using the structured lighting technique.The proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing coefficients.The experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

K-nearest neighborhood based integration of time-of-flight cameras and passive stereo for high-accuracy depth maps?

Both time-of-flight(ToF) cameras and passive stereo can provide the depth information for their corresponding captured real scenes, but they have innate limitations. ToF cameras and passive stereo are intrinsically complementary for certain tasks. It is desirable to appropriately leverage all the available information by ToF cameras and passive stereo. Although some fusion methods have been presented recently, they fail to consider ToF reliability detection and ToF based improvement of passive stereo. As a result, this study proposes an approach to integrating ToF cameras and passive stereo to obtain high-accuracy depth maps. The main contributions are:(1) An energy cost function is devised to use data from ToF cameras to boost the stereo matching of passive stereo;(2) A fusion method is used to combine the depth information from both ToF cameras and passive stereo to obtain high-accuracy depth maps. Experiments show that the proposed approach achieves improved results with high accuracy and robustness.

Interactive lighting editing system for single indoor low-light scene images with corresponding depth maps

We propose a novel interactive lighting editing system for lighting a single indoor RGB image based on spherical harmonic lighting.It allows users to intuitively edit illumination and relight the complicated low-light indoor scene.Our method not only achieves plausible global relighting but also enhances the local details of the complicated scene according to the spatially-varying spherical harmonic lighting,which only requires a single RGB image along with a corresponding depth map.To this end,we first present a joint optimization algorithm,which is based on the geometric optimization of the depth map and intrinsic image decomposition avoiding texture-copy,for refining the depth map and obtaining the shading map.Then we propose a lighting estimation method based on spherical harmonic lighting,which not only achieves the global illumination estimation of the scene,but also further enhances local details of the complicated scene.Finally,we use a simple and intuitive interactive method to edit the environment lighting map to adjust lighting and relight the scene.Through extensive experimental results,we demonstrate that our proposed approach is simple and intuitive for relighting the low-light indoor scene,and achieve state-of-the-art results.

INPAINTING ALGORITHM FOR KINECT DEPTH MAP BASED ON FOREGROUND SEGMENTATION

The depth information of the scene indicates the distance between the object and the camera,and depth extraction is a key technology in 3D video system.The emergence of Kinect makes the high resolution depth map capturing possible.However,the depth map captured by Kinect can not be directly used due to the existing holes and noises,which needs to be repaired.We propose a texture combined inpainting algorithm in this paper.Firstly,the foreground is segmented combined with the color characteristics of the texture image to repair the foreground of the depth map.Secondly,region growing is used to determine the match region of the hole in the depth map,and to accurately position the match region according to the texture information.Then the match region is weighted to fill the hole.Finally,a Gaussian filter is used to remove the noise in the depth map.Experimental results show that the proposed method can effectively repair the holes existing in the original depth map and get an accurate and smooth depth map,which can be used to render a virtual image with good quality.

Co-axial depth sensor with an extended depth range for AR/VR applications

Background Depth sensor is an essential element in virtual and augmented reality devices to digitalize users'environment in real time.The current popular technologies include the stereo,structured light,and Time-of-Flight(ToF).The stereo and structured light method require a baseline separation between multiple sensors for depth sensing,and both suffer from a limited measurement range.The ToF depth sensors have the largest depth range but the lowest depth map resolution.To overcome these problems,we propose a co-axial depth map sensor which is potentially more compact and cost-effective than conventional structured light depth cameras.Meanwhile,it can extend the depth range while maintaining a high depth map resolution.Also,it provides a high-resolution 2 D image along with the 3 D depth map.Methods This depth sensor is constructed with a projection path and an imaging path.Those two paths are combined by a beamsplitter for a co-axial design.In the projection path,a cylindrical lens is inserted to add extra power in one direction which creates an astigmatic pattern.For depth measurement,the astigmatic pattern is projected onto the test scene,and then the depth information can be calculated from the contrast change of the reflected pattern image in two orthogonal directions.To extend the depth measurement range,we use an electronically focus tunable lens at the system stop and tune the power to implement an extended depth range without compromising depth resolution.Results In the depth measurement simulation,we project a resolution target onto a white screen which is moving along the optical axis and then tune the focus tunable lens power for three depth measurement subranges,namely,near,middle and far.In each sub-range,as the test screen moves away from the depth sensor,the horizontal contrast keeps increasing while the vertical contrast keeps decreasing in the reflected image.Therefore,the depth information can be obtained by computing the contrast ratio between features in orthogonal directions.Conclusions The proposed depth map sensor could implement depth measurement for an extended depth range with a co-axial design.

DepthGAN: GAN-based depth generation from semantic layouts

Existing GAN-based generative methodsare typically used for semantic image synthesis. Wepose the question of whether GAN-based architecturescan generate plausible depth maps and find thatexisting methods have difficulty in generating depthmaps which reasonably represent 3D scene structuredue to the lack of global geometric correlations.Thus, we propose DepthGAN, a novel method ofgenerating a depth map using a semantic layout asinput to aid construction, and manipulation of wellstructured 3D scene point clouds. Specifically, wefirst build a feature generation model with a cascadeof semantically-aware transformer blocks to obtaindepth features with global structural information.For our semantically aware transformer block, wepropose a mixed attention module and a semanticallyaware layer normalization module to better exploitsemantic consistency for depth features generation.Moreover, we present a novel semantically weighteddepth synthesis module, which generates adaptivedepth intervals for the current scene. We generate thefinal depth map by using a weighted combination ofsemantically aware depth weights for different depthranges. In this manner, we obtain a more accuratedepth map. Extensive experiments on indoor andoutdoor datasets demonstrate that DepthGAN achievessuperior results both quantitatively and visually for thedepth generation task.

Vision Based Hand Gesture Recognition Using 3D Shape Context

Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient.The representation of hand gestures is critical for recognition.In this paper,we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition.The depth maps of hand gestures captured via the Kinect sensors are used in our method,where the 3D hand shapes can be segmented from the cluttered backgrounds.To extract the pattern of salient 3D shape features,we propose a new descriptor-3D Shape Context,for 3D hand gesture representation.The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition.The description of all the 3D points constructs the hand gesture representation,and hand gesture recognition is explored via dynamic time warping algorithm.Extensive experiments are conducted on multiple benchmark datasets.The experimental results verify that the proposed method is robust to noise,articulated variations,and rigid transformations.Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.

基于空间句法的不同历史时期谐趣园空间组织特征分析

谐趣园是现存古典园林中一座典型的园中之园。自乾隆时期初建以来,谐趣园经历了多次不同规模的改建与修缮。该文首先基于样式房图档、陈设档等史料对嘉庆、光绪和现状三个时期的谐趣园格局开展考证和复原,并借助空间句法理论的Depth map软件对园林空间的变迁开展定量分析,比较整合度、深度、连接度等反映空间组织特征的指标,用定量的方式解读其空间组织特征,置于历史背景下思考造成空间组织变迁的原因。

Recent MPEG Standardization Activities on 3D Video Coding

The Moving Picture Experts Group （MPEG） has been developing a 3D video （3DV） coding standard for depth-based 3DV data representations, especially for multiview video plus depth （MVD） format. With MVD, depth-image-based rendering （DIBR）is used to synthesize virtual views that are based on a few transmitted pairs of texture and depth data. In this paper, we discuss ongoing 3DV standardization and summarize coding tools proposed in the responses to MPEG＇ s call for proposals on 3DV coding.

基于空间句法的教学楼空间组织特征分析——以中国美术学院象山校区16号教学楼二层为例

校园建筑内部功能空间的布置形式、空间的视域渗透关系与可达性等因素在一定程度上对师生的使用行为存在潜在影响。许多既有的校园建筑建成历史悠久,其空间与使用人群行为活动规律存在着矛盾。本文基于空间句法,以中国美术学院象山校区16号教学楼二层的空间为例,利用Depth map软件,采用定性和定量相结合的方法对其内部空间进行模拟、分析与评价,并提出可行性改造建议。

Sparse Representations-based depth images quality assessment

The conventional 2D metrics can be used for measuring the quality of depth maps,but none of them is considered to be efficient and is not accurate when used for evaluating 3D quality.In this paper,we propose a new full reference objective metric,called Sparse Representations-Mean Squared Error(SR-MSE),which efficiently evaluates the depth maps compression distortions.It adaptively models the reference and compressed depth maps in a mixed redundant transform domain dedicated to depth features.Then,it computes the mean squared error between the sparse coefficients issued from this modeling.As a benchmark of quality assessment,we perform a subjective evaluation test for depth maps compressed using the latest 3D High Efficiency Video Coding standard at various bitrates.We compare the subjective results with the proposed and conventional objective metrics.Experimental results demonstrate that the proposed SR-MSE,compared to the conventional image quality assessment metrics,yields the highest correlated scores to the subjective ones.

Depth map resolution improvement for 3D range-intensity correlation laser imaging

This Letter proposes a high bit-depth coding method to improve depth map resolution and render it suitable to human-eye observation in 3D range-intensity correlation laser imaging. In this method, a high bit-depth CCD camera with a nanosecond-sealed gated intensifier is used as an image sensor; subsequently two high bit-depth gate images with specific range-intensity profiles are obtained to establish the gray depth map and finally the gray depth map is encoded by an equidensity pseudocolor. With this method, a color depth map is generated with higher range resolution. In our experimental work, the range resolution of the depth map is improved by a factor of 1.67.

Performance study of feature descriptors for human detection on depth map

Depth map contains the space information of objects and is almost free from the influence of light,and it attracts many research interests in the field of machine vision used for human detection.Therefore,hunting a suitable image feature for human detection on depth map is rather attractive.In this paper,we evaluate the performance of the typical features on depth map.A depth map dataset containing various indoor scenes with human is constructed by using Microsoft’s Kinect camera as a quantitative benchmark for the study of methods of human detection on depth map.The depth map is smoothed with pixel filtering and context filtering so as to reduce particulate noise.Then,the performance of five image features and a new feature is studied and compared for human detection on the dataset through theoretic analysis and simulation experiments.Results show that the new feature outperforms other descriptors.

Sparse RGB-D images create a real thing:A flexible voxel based 3D reconstruction pipeline for single object

Reconstructing 3D models for single objects with complex backgrounds has wide applications like 3D printing,AR/VR,and so on.It is necessary to consider the tradeoff between capturing data at low cost and getting high-quality reconstruction results.In this work,we propose a voxel-based modeling pipeline with sparse RGB-D images to effectively and efficiently reconstruct a single real object without the geometrical post-processing operation on background removal.First,referring to the idea of VisualHull,useless and inconsistent voxels of a targeted object are clipped.It helps focus on the target object and rectify the voxel projection information.Second,a modified TSDF calculation and voxel filling operations are proposed to alleviate the problem of depth missing in the depth images.They can improve TSDF value completeness for voxels on the surface of the object.After the mesh is generated by the MarchingCube,texture mapping is optimized with view selection,color optimization,and camera parameters fine-tuning.Experiments on Kinect capturing dataset,TUM public dataset,and virtual environment dataset validate the effectiveness and flexibility of our proposed pipeline.

The ENSO Events in the Tropical Pacific and Dipole Events in the Indian Ocean

A depth map （close to that of the thermocline as defined by 20 ℃） of climatically maximum seatemperature anomaly was created at the subsurface of the tropical Pacific and Indian Ocean, based on which the evolving sea-temperature anomaly at this depth map from 1960 to 2000 was statistically analyzed. It is noted that the evolving sea temperature anomaly at this depth map can be better analyzed than the evolving sea surface one. For example, during the ENSO event in the tropical Pacific, the seatemperature anomaly signals travel counter-clockwise within the range of 10°S-10°N, and while moving, the signals change in intensity or even type. If Dipole is used in the tropical Indian Ocean for analyzing the depth map of maximum sea-temperature anomaly, the sea-temperature anomalies of the eastern and western Indian Oceans would be negatively correlated in statistical sense （Dipole in real physical sense）, which is unlike the sea surface temperature anomaly based analysis which demonstrates that the inter-annual positive and negative changes only occur on the gradients of the western and eastern temperature anomalies. Further analysis shows that the development of ENSO and Dipole has a time lag features statistically, with the sea-temperature anomaly in the eastern equatorial Pacific changing earlier （by three months or so）. And the linkage between these two changes is a pair of coupled evolving Walker circulations that move reversely in the equatorial Pacific and Indian Oceans.