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RGB-D data, being homogeneous cross-modal data, demonstrates significant correlations among data elements. However, current research focuses only on a uni-directional pattern of cross-modal contextual information, neglecting the exploration of bi-...

The field of RGBD object tracking is gaining increasing attention due to the ability of depth sensors to capture spatial information. Consequently, the inclusion of depth modality enables trackers to perform better in scenarios with occlusion, ...${}^{}$${}^{\mathrm{}}$${}^{\mathrm{}}$

• We propose a pyramid network with progressive guidance structures.
• Global and local information of features is supplemented by the pyramid structure.
• The MEFM can reduce redundant information of depth maps and aggregate multiscale ...

In RGB-D (red–green–blue and depth) scene semantic segmentation, depth maps provide rich spatial information to RGB images to achieve high performance. However, properly aggregating depth information and reducing noise and information loss during ...

In this paper, we propose a traditional semi-dense direct visual odometry (VO) based on our preliminary study using low-order Gaussian derivative functions for solving a VO problem with pure frame-by-frame point tracking. With the off-line fitting ...

RGB-D cameras mounted on moving agricultural robotic platforms provide detailed information about both appearance and volume of plants. Those images can be analysed by means of deep segmentation models; however, such methods usually dismiss depth ...

The success of vision transformer demonstrates that the transformer structure is also suitable for various vision tasks, including high-level classification tasks and low-level dense prediction tasks. Salient object detection (SOD) is a pixel-...

This paper proposes a new approach that acquires camera parameters and generates an integrated 3D joint using an RGB-D camera network distributed in an arbitrary location in space. The proposed technique consists of three steps. In the first step,...

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• Performing camera calibration using incomplete or partial joints obtained from images output from cameras installed in irregular positions.
• Temporal improvement of the precision of transformation parameters obtained through camera ...

Camouflaged object detection (COD) presents a persistent challenge in accurately identifying objects that seamlessly blend into their surroundings. However, most existing COD models overlook the fact that visual systems operate within a genuine ...

• An advanced RGB-D camouflaged object detection model is proposed.
• Knowledge about depth can greatly assist in recognizing camouflaged objects.
• Presentation of a fusion method that adaptively incorporates the generated depth.

RGB-D salient object detection (SOD) which aims to detect the prominent regions in figures has attracted much attention recently. It jointly models the RGB and depth information. However, existing methods explore cross-modality information from ...

Point clouds consist of 3D data points and are among the most considerable data formats for 3D representations. Their popularity is due to their broad application areas, such as robotics and autonomous driving, and their employment in basic 3D ...

One of the most important tasks of the current decade is the mass introduction of telepresence services, including the use of holographic copies of a person, robotic avatars, manipulator devices, as well as the implementation and deployment of their ...

The integration of dual-modal features has been pivotal in advancing RGB-Depth (RGB-D) tracking. However, current trackers are less efficient and focus solely on single-level features, resulting in weaker robustness in fusion and slower speeds that fail ...

• We propose Cross-Modal Transformer (CMFormer), a Transformer-based cross-modal semantic segmentation model, which achieves better cross-modal information interaction by capturing long-range contextual dependencies. The CMFormer includes ...

Scene understanding in a production workshop is an important technology to improve its intelligence level, semantic segmentation of production workshop objects is an effective method for realizing scene understanding. Since the varieties of ...

RGB-D object recognition systems improve their predictive performances by fusing color and depth information, outperforming neural network architectures that rely solely on colors. While RGB-D systems are expected to be more robust to adversarial ...

• We assess the performance of a state-of-art system for object detection based on color and depth features.
• We explain why RGB features are more vulnerable to attacks than depth features.
• We develop a defense against adversarial ...

A smartphone with both colour and time of flight depth cameras is used for automated grape yield estimation of Chardonnay grapes. A new technique is developed to automatically identify grape berries in the smartphone’s depth maps. This utilises ...

• Grape yield estimation using a smartphone’s colour and time of flight depth cameras.
• Peaks in the depth map occur at berry locations due to diffuse scattering of light.
• Grape berries 3D locations are automatically detected using ...

Neural Radiance Fields (NeRFs) have gained widespread recognition as a highly effective technique for representing 3D reconstructions of objects and scenes derived from sets of images. Despite their efficiency, NeRF models can pose challenges in ...

RGB-D semantic segmentation is crucial for robots to understand scenes. Most existing methods take depth information as an additional input, leading to cross-modal semantic segmentation networks that cannot achieve the purpose of multi-scale ...

vSLAM (Visual Simultaneous Localization and Mapping) is a fundamental function in various robot applications. With the development of downstream applications, there is an increasing challenge for scene semantic understanding and stable operation ...

Existing RGB-D salient object detection methods generally rely on the dual-encoder structure for RGB and depth feature extraction. However, we observe that the encoders in such models are often not adequately trained to obtain superior feature ...

Saliency detection refers to accurately positioning and extracting significant objects or regions in the image. Most effective object detection methods are based on RGB-D and adopt the dual-flow architecture with RGB and depth symmetry. At the ...