NeRF represents scenes as continuous 3D volumes. Instead of discrete 3D meshes or point clouds, it defines a function that calculates color and density values for any 3D point within the scene. By training the neural network on multiple scene images captured from different viewpoints, NeRF learns to generate consistent and accurate representations that align…

Finer control over the visual characteristics and notions represented in a produced picture is typically required by artistic users of text-to-image diffusion models, which is presently not achievable. It can be challenging to accurately modify continuous qualities, such as an individual’s age or the intensity of the weather, using simple text prompts. This constraint makes…

Providing a virtual environment that matches the actual world, the recent widespread rise of 3D applications, including metaverse, VR/AR, video games, and physical simulators, has improved human lifestyle and increased productive efficiency. These programs are based on triangle meshes, which stand in for the intricate geometry of actual environments. Most current 3D applications rely on…

Researchers from Shanghai AI Laboratory, Fudan University, Northwestern Polytechnical University, and The Hong Kong University of Science and Technology have collaborated to develop a 3D Gaussian representation-based Simultaneous Localization and Mapping (SLAM) system named GS-SLAM. The goal of the plan is to achieve a balance between accuracy and efficiency. GS-SLAM uses a real-time differentiable splatting…

Following another vehicle is the most common and basic driving activity. Following other cars safely lessens collisions and makes traffic flow more predictable. When drivers follow other vehicles on the road, the appropriate car-following model represents this behavior mathematically or computationally. The availability of real-world driving data and developments in machine learning have largely contributed…

OpenAI has been at the forefront of the latest advancements in AI, with its highly competent models like GPT and DALLE. When released, GPT-3 was a one-of-its-kind model with great language processing capabilities such as text summarization, sentence completion, and many others. The release of its successor, GPT-4, marked a significant shift in how we…

In the quickly advancing field of Artificial Intelligence (AI), Deep Learning is becoming significantly more popular and stepping into every industry to make lives easier. Simultaneous Localization and Mapping (SLAM) in AI, which is an essential component of robots, driverless vehicles, and augmented reality systems, has been experiencing revolutionary advancements recently. SLAM involves reconstructing the…

MeshGPT is proposed by researchers from the Technical University of Munich, Politecnico di Torino, AUDI AG as a method for autoregressive generating triangle meshes, leveraging a GPT-based architecture trained on a learned vocabulary of triangle sequences. This approach uses a geometric vocabulary and latent geometric tokens to represent triangles, producing coherent, clean, compact meshes with…

The development of commercial mixed reality platforms and the quick advancement of 3D graphics technology have made the creation of high-quality 3D scenes one of the main challenges in computer vision. This calls for the capacity to convert any input text, RGB, and RGBD pictures, for example, into a variety of realistic and varied 3D…

Big vision-language models, or LVLMs, can interpret visual cues and provide easy replies for users to interact with. This is accomplished by skillfully fusing large language models (LLMs) with large-scale visual instruction finetuning. Nevertheless, LVLMs only need hand-crafted or LLM-generated datasets for alignment by supervised fine-tuning (SFT). Although it works well to change LVLMs from…