Latest Trends in 3d Reconstruction

3D Gaussian Splatting (3DGS) is becoming the new standard for 3D reconstruction, rapidly transforming fields like Robotics, Autonomous Vehicles, AR/VR, Gaming, and VFX. With its ability to deliver photorealistic, real-time rendering while capturing large-scale scenes with minimal artifacts, 3DGS is solving complex problems across industries—all without relying on neural networks. In this article, we break down the Gaussian Splatting paper, explore the key equations, and explain how it achieves its unmatched performance. We also guide you through training your own data using nerf-studio's gSplat and share tips to get the best results. If you're working on 3D reconstruction or visual computing, this is the resource you need to stay ahead! https://buff.ly/3ZKhzrG

🚀 Triplane Meets Gaussian Splatting; this innovative generative modeling technique from Tsinghua University and Vast.ai has some qualitatively interesting 3D reconstruction results. This framework leverages a hybrid Triplane-Gaussian representation to rapidly reconstruct detailed 3D models from single images. By deploying two transformer-based networks, it overcomes the slow optimization and rendering processes typical in current 3D generative models. The unique combination of triplane decoding and Gaussian feature query allows for faster rendering speeds and superior rendering quality compared to both implicit and explicit representations. A couple things I find interesting: 1) Perspective angle image sources tend to result in better 3D reconstructions compared to flat. 2) This is yet another example of blending modalities to achieve better results. 🔗 Discover their Project Page: https://lnkd.in/eGGTVX2q 📚 Dive into their research: https://lnkd.in/eszrVQk9 🤗 Demo: https://lnkd.in/eDKfJYGq For more cutting-edge AI and 3D reconstruction insights ��� 👉 Follow Orbis Tabula #3DReconstruction #TransformerNetworks #GaussianSplatting

🧠 CAST: Component-Aligned 3D Scene Reconstruction from a Single RGB Image Just explored an impressive research project from ShanghaiTech University and Deemos Technology: CAST, a novel approach to reconstructing high-quality 3D scenes from a single RGB image. 🔍 Key Highlights: Object-Level Segmentation & Depth Estimation: Extracts detailed 2D segmentation and relative depth information. GPT-Based Spatial Analysis: Utilizes a GPT-based model to understand inter-object spatial relationships. Occlusion-Aware 3D Generation: Independently generates each object's full geometry, addressing occlusions and partial data. Physics-Aware Correction: Ensures physical consistency and spatial coherence using a fine-grained relation graph and Signed Distance Fields (SDF). 🎯 Applications: Virtual content creation for games and films. Robotics: Facilitates efficient real-to-simulation workflows. Digital twins and immersive environments. 🔗 Dive deeper into the project here: https://lnkd.in/gHjXBKJE #3DReconstruction #ComputerVision #AI #DigitalTwins #Robotics #CAST #generativeai