Posts by Pier Luigi Dovesi

Semantic Fencing of Video Streams Using Embedding Splits from Vision Foundation Models

In this blog, we present a novel approach for semantically splitting vision datasets into training, validation, and test sets. Instead of relying on ad hoc metadata rules or random shuffles, we use embeddings to reason directly about similarity in latent space and construct splits that better reflect true generalization.

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AMD-Powered 3D Gaussian Splatting for Autonomous Driving Scenes

3D Gaussian Splatting (3DGS) is an innovative, explicit scene representation and rendering technique. It reconstructs photorealistic 3D environments from a set of images of the scene from a variety of angles. It represents a scene as a vast, learnable collection of 3D Gaussians, which are optimized with backpropagation using a differentiable rasterizer. This pipeline enables real-time novel view synthesis of the scene – generating images of the scene from previously unseen angles. It also permits easy scene editing by moving, copying, recolouring, etc. parts of the reconstructed 3D structure.

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Styled Text Image Generation with Eruku on AMD

Producing text images where text is both readable and controllable while faithfully matching a target visual style is a challenging problem. It has broad applications ranging from synthetic handwritten text generation to graphic design. In these settings, you need more than plausible images; you need precise control over both text content and visual fidelity. This is where Eruku[1] stands out.

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3D Scene Reconstruction from the Inside: Explore the Mathematics Behind gsplat

3D Gaussian Splatting (3DGS) reconstructs 3D scenes from multiple 2D images and renders novel views in real time. In this blog, which serves as a follow up to a previous post, Elevating 3D Scene Rendering with GSplat, you will learn the core mathematics and the practical library components behind 3DGS using gsplat.

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Elevating 3D Scene Rendering with GSplat

In this blog we explore how to use GSplat, a GPU-optimized Python library for training and rendering 3DGS models, on AMD devices. This tutorial will guide you through training a model of a scene from a set of captured images, which will then allow you to render novel views of the scene. We use a port of the original GSplat code that has been optimized for AMD GPUs. The examples used throughout this blog were trained and rendered using an AMD MI300X GPU.

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