We present Real-time Gaussian SLAM (RTG-SLAM), a real-time 3D reconstruction system with an RGBD camera for large-scale environments using Gaussian splatting. The system features a compact Gaussian representation and a highly efficient on-the-fly ...

Acquiring depth data has become easily achievable with advancements in depth sensing and depth estimation technologies. As a result, obtaining a depth stream to describe the topology of a corresponding video stream has been considerably simplified. ...

Automation is increasingly at the forefront of transportation research, with the potential to bring fully autonomous vehicles to our roads in the coming years. This comprehensive survey provides a holistic look at the essential components and ...

Deep fake technology paves the way for a new generation of super realistic artificial content. While this opens the door to extraordinary new applications, the malicious use of deepfakes allows for far more realistic disinformation attacks than ...

• Integrating depth and RGB improves the accuracy and robustness of deepfake detectors.
• Late fusion is the best fusion strategy for integrating RGB and depth features.
• We guide the integration of depth information via a self-...

This paper proposes a depth image symbiosis model to solve the problem of insufficient depth image quality in animated advertising. The model uses image surface information and image edge cues as the main guidance information to obtain image symbiosis ...

Immersive technologies such as virtual reality have enabled novel forms of education and training, where students can learn new skills in simulated environments. But some specialized training procedures, e.g. ESA-certified soldering, still involve real-...

Visual perception in RGBD (Red-Green-Blue and Depth) camera technology allows the camera to provide color image and depth structure information simultaneously from the surface of the scanned object. Based on this technology, data from an RGBD ...

The multi-modality or multi-stream-based convolution neural network is the recent trend in saliency computation, which is receiving tremendous research interest. The previous models used modality-based independent fusion or cross-modality-based ...$\mathrm{}\mathrm{}$$\mathrm{}\mathrm{}$

Significant improvement has been noticed in salient object detection by multi-modal cross-complementary fusion between Depth and RGB features. The multi-modal feature extracting backbone of existing networks cannot extract complex RGB and color ...

3D human reconstruction is an important technology connecting the real world and the virtual world, but most of previous work needs expensive computing resources, making it difficult in real-time scenarios. We propose a lightweight human body ...

Elderly people in care homes face a great lack of contact with their families and loved ones. Social isolation and loneliness are detrimental factors for older people in their health condition, cognitive impairment and quality of life. We previously ...

This paper is focused on the proposal and verification of the RGBD mapping system for a small, low-cost mobile robot. The solution's requested properties were easy to replicate and easy to extend for further development on commonly available ...

Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly rely on the RGB information to distinguish the salient objects, which ...

This article describes the Blackbird unmanned aerial vehicle (UAV) Dataset, a large-scale suite of sensor data and corresponding ground truth from a custom-built quadrotor platform equipped with an inertial measurement unit (IMU), rotor tachometers, and ...

The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision ...

Most of the deep convolutional neural networks (CNNs) based RGBD saliency models either regard the RGB and depth cues as the same status or trust the depth information excessively. However, they ignore that the low-quality depth map is ...

Convolutional neural networks have gradually dominated the field of image semantic segmentation, and have achieved good results in 2D image semantic segmentation tasks. However, the 2D semantic segmentation algorithm based on CNN is still unsatisfactory ...

Background subtraction from color and depth data is a fundamental task for video surveillance applications that use data acquired by RGBD sensors. We present a method that adopts a self-organizing neural background model previously adopted for RGB videos ...

Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the existing co-saliency methods focusing on ...

In this paper, a new activity recognition technique is introduced based on the gray level co-occurrence matrices (GLCM) from a 3D dense optical flow of the input RGB and Depth videos. These matrices are one of the earliest techniques used for image ...