V-Ray Next is made up of two rendering engines: a traditional CPU based renderer, as well as a GPU-based hybrid engine that can run on both GPUs and CPUs for extra performance. With the launch of NVIDIA’s new GeForce RTX “SUPER” series of video cards, we are taking a look at how the whole RTX lineup performs on the GPU side of V-Ray Next.
OctaneRender is a GPU-based rendering engine, utilizing the CUDA programming language on NVIDIA-based graphics cards. With the launch of NVIDIA’s new GeForce RTX “SUPER” series of video cards, we are taking a look at how the whole RTX lineup performs on both the current OctaneRender 4 and the upcoming 2019 release which adds support for RTX technology and greatly increased rendering speeds.
Redshift is a GPU-based rendering engine, now owned by Maxon and available bundled with Cinema 4D – as well as in the form of plug-ins for other 3D applications. It was written to use NVIDIA’s CUDA graphics programming language, and since NVIDIA recently refreshed their GeForce series with new 2060, 2070, and 2080 “SUPER” cards we thought it would be a good time to re-test the whole RTX lineup.
AMD’s new Ryzen 3rd generation processors feature both an increase in core count and per-core performance, allowing them to rival and sometimes beat Intel’s mainstream Core processors in professional applications. In this article we are going to take a look at how these new CPUs handle RealityCapture, which benefits from both core count and clock speed at various points throughout its workflow.
AMD’s new Ryzen 3rd generation processors feature both an increase in core count and per-core performance, both of which directly improve rendering speeds in Cinema 4D. In this article we will take a look at how they stack up to other AMD and Intel processors in this application, focusing exclusively on rendering performance via Cinebench R20.
AMD’s new Ryzen 3rd generation processors feature both an increase in core count and per-core performance, both of which directly improve rendering speeds in V-Ray Next. In this article we will take a look at how they stack up to other AMD and Intel processors in this application, both in the pure CPU and GPU+CPU render pipelines.
RealityCapture, like other photogrammetry applications, is built to take a batch of photographs and turn them into digital, 3D models. The algorithms used during that process are designed to be “out of core”, meaning that not all of the data has to be loaded into system memory (RAM) at the same time – allowing for full processing without requiring a ton of available memory. Having more RAM can still be beneficial, though, so we decided to test how much impact it has on performance.
Metashape, formerly known as PhotoScan, is a photogrammetry program that takes a set of images and combines them to create a 3D model or map. Processing of those images into point clouds and 3D meshes/textures is time-consuming, heavily using a computer’s CPU and GPU. We have recently updated our benchmark tools to support Metashape, along with new image sets to test, so now we are taking a look at how different NVIDIA and AMD video cards perform in this application.
Pix4D is an advanced photogrammetry application, suited to a wide range of uses, with a focus on handling images captured by drone cameras. Processing of those images into point clouds and 3D meshes/textures is time-consuming, heavily using a computer’s CPU and GPU. We have recently updated our benchmark tools for Pix4D, and published articles looking at CPU and GPU performance on version 4.3 – but in the midst of our testing, 4.4 was released. Is it any faster?
RealityCapture, like other photogrammetry applications, is built to take a batch of photographs and turn them into digital, 3D models. The many steps involved in that process can take a lot of time, and utilize both the CPU and GPU at different points. We recently put together a benchmark tool for RealityCapture, and after looking at processor performance last week we are now diving into a comparison of the current NVIDIA GeForce and Titan video cards.
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