The Witcher 3 Mod Improves Evironment Textures and Draw Distance

Pretty much anyone can agree that The Witcher 3 is one of the best looking games that were released this year and that CD Project RED did a really great job on the game. But that doesn’t mean that modders can’t improve further on the game. Games with modding abilities usually draw a big crowd as it opens up to nearly endless possibilities and fixes on things that the original game might have mixed. One place that CD Project RED didn’t pay so much attention to were the environmental textures such as rocks, wood crates, and sacks, and that is where the first new mod comes into play.

The first new mod is the Environmental HD texture pack by modder Halk Hogan PL. It updates the textures of most rocks, wood crates, sacks and checkered floor in the amazing The Wither 3 and best of all, it shouldn’t affect your performance. According to the creator, the new mod didn’t affect the performance on this AMD Radeon HD7970 with 3GB memory but warned that 2GB VRAM models might experience a slight performance hit. That, however, is still unconfirmed and it might run just as well with 2GB VRAM. You can find the mod here.

The second new mod increases the overall draw distance and level of detail for a lot of in-game objects and it was created by the modder sjbox. The mod can increase the draw distance and detail levels by 10 to 40%, but it will naturally have some impact on the performance. This shouldn’t be an issue for high-end gaming systems as it reportedly only is around 5 FPS in crowded areas. When the GPU has to draw more objects, it will need some more power. That’s just logic. You can find the mod here.

You can see comparison screenshots with the two new The Wither 3 mods below:

Environmental HD Texture Mod

Draw Distance and Detail Level Mod (modded then unmodded)

Complex Algorithm To Accurately Identify Objects Including Human Faces

Computers that can identify objects seem a thing from the future. Apparently, it is more close to reality than any of us think. Birmingham Young University from Provo, US – has found a way to make computers identify objects without the need of a human helping hand.

According to Dah-Jye Lee, BUY engineer, algorithms have become so advanced that they can make a piece of software identify objects by themselves from images and even videos. Lee is the founder of this algorithm and from what he describes, it is based on the computer making decisions on its own based on the shapes identified on the images or videos analysed.

“In most cases, people are in charge of deciding what features to focus on and they then write the algorithm based off that,” said Lee, a professor of electrical and computer engineering. “With our algorithm, we give it a set of images and let the computer decide which features are important.”

Lee’s algorithm is said to learn on its own, just as a child learns to distinguish a cat from a dog. He explains that instead of teaching a child the difference between the latter, we are better off showing the two images and let the child distinguish them on his or her own. Just like a child, the algorithm has been shown four image datasets from CalTech, namely motorbikes, faces, airplanes and cars, having the algorithm output 100% accurate results on each of the datasets. However, the algorithm had a lower rate of success with human faces, being able to accurately distinguish 99.4%, but still gave a better result than other object recognition systems.

“It’s very comparable to other object recognition algorithms for accuracy, but, we don’t need humans to be involved,” Lee said. “You don’t have to reinvent the wheel each time. You just run it.”

Professor Lee mentioned that the highly complicated algorithm may be used in a variety of tasks, from detecting invasive fish species to identifying flaws in produce such as apples on a production line. However, the complexity of the algorithm can go way beyond that.

Thank you Birmingham Young University for providing us with this information
Images courtesy of Birmingham Young University