Collov AI’s Post

Harnessing Gaussian Splat Technology for Infrastructure Insight At ToPa 3D, we leverage Gaussian Splat technology to provide a clearer, more detailed picture of infrastructure for inspection, as-built documentation, and quality control. This AI-powered technique delivers smooth, high-resolution visualizations that reveal every structural detail, allowing teams to identify issues, verify construction accuracy, and ensure project quality. Whether you’re managing complex infrastructure or conducting routine inspections, Gaussian Splat enhances visibility and precision for better decision-making. ToPa 3D – Reality Capture Expertise for Precision, Efficiency, and Innovation. #InfrastructureInspection #GaussianSplat #AsBuilt #QualityControl #RealityCapture #ToPa3D

Maximizing Asset Clarity with AI in Reality Capture Our use of AI in 3D modeling drastically improves the efficiency of our reality capture services. By leveraging machine learning algorithms, we can quickly analyze vast datasets and pinpoint key structural elements that need attention. This allows your team to focus on high-impact areas, minimizing time spent on manual data interpretation. Our AI-powered tools provide deeper understanding of complex assets, delivering high-quality results in a fraction of the time. ToPa 3D – Reality Capture Expertise for Precision, Efficiency, and Innovation in AEC, Data Centers, and Historic Preservation. #AIinConstruction #RealityCapture #3DTechnology #PrecisionModeling #ProjectEfficiency

InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models 📣 📣 📣 InstantMesh is a novel framework for generating 3D meshes from a single image instantly, integrating state-of-the-art quality with scalable training. It combines a multiview diffusion model and a sparse-view reconstruction model, enabling the creation of varied 3D assets in just 10 seconds. The framework has shown superior performance on public datasets compared to other image-to-3D methods, and the developers have released the code, weights, and demos to benefit the 3D generative AI community. paper: https://lnkd.in/dfmQX8xH github: https://lnkd.in/dStHEyPu demo: https://lnkd.in/dwdKyiv4 comfyui: https://lnkd.in/gRcAEQtW #InstantMesh #3DGeneration #AIModeling #MeshCreation #GenerativeAI #3DArt #TechInnovation #AIResearch #DigitalAssets #OpenSourceAI #ComfyUI

At 3DforScience, we are excited to showcase our latest project, which combines AI with our expertise to create stunning 3D images. AI processes vast amounts of scientific data, automating complex tasks like texture mapping, allowing our artists to focus on creativity. AI enhances realism by generating intricate textures, lighting, and shading, crucial for accurately depicting biological structures. It also enables rapid iteration and optimization of designs, ensuring the best visual representation of scientific concepts. Additionally, AI's predictive modeling can visualize complex phenomena, offering new insights. Overall, AI helps us push the limits of scientific visualization, making complex ideas clear and engaging. We are taking science to new levels! To discover more about us, check out our page: https://lnkd.in/eNHDsTvf #AIChallenge #3DAnimation #Science #Biotechnology #Health #Innovation #3DforScience

This AI Research from Stability AI and Tripo AI Introduces TripoSR Model for Fast FeedForward 3D Generation from a Single Image https://lnkd.in/e7FYd64T In the realm of 3D generative AI, the boundaries between 3D generation and 3D reconstruction from a small number of views have started to blur. This convergence is propelled by a series of breakthroughs, including the emergence of large-scale public 3D datasets and advancements in generative model topologies There has been new research into using 2D diffusion models to generate 3D objects from input photos or text prompts to circumvent the lack of 3D training data. One example is DreamFusion, which pioneered score distillation sampling (SDS) by optimizing 3D models using a 2D diffusion model. To generate detailed 3D objects, this method is a game-changer since it uses 2D priors for 3D production. However, because of the high computational and optimization requirements and the difficulty in accurately managing the output models,...

The Benefits of Virtual Sensor Representation in Engineering 🎥 Watch the full episode here: https://lnkd.in/exVXCb6J 🌎 Connect with Paola on LinkedIn: https://lnkd.in/ek2YPPTx 👉 AI with Model-Based Design: https://lnkd.in/eEWKJu-3 #engineering #mathworks #artificialintelligence

🌟 CVPR 2024: SpatialTracker, a highlight of this year's conference and a breakthrough in computer vision technology. Here’s why SpatialTracker is a game-changer in tracking 2D pixels in 3D space using monocular videos: ✅ 3D Tracking: Upgrades from 2D to 3D tracking, offering enhanced spatial insights. ✅ Triplane Representation: Condenses frame data into three 2D planes for efficient 3D feature handling. ✅ Trajectory Prediction: Employs transformers for accurate 3D trajectory estimation across videos. ✅ ARAP Constraints: Utilizes rigid motion constraints to improve tracking consistency. ✅ Rigidity Clustering: Clusters pixels into rigid parts for precise component tracking. ➡ Paper: https://lnkd.in/gvFzyj5d ➡ Code: https://lnkd.in/gPS_qtdi   #computervision #artificialintelligence #machinelearning #deeplearning #AI #SpatialTracker #3DTracking #cvpr #opensource

DELTA: A Novel AI Method that Efficiently (10x Faster) Tracks Every Pixel in 3D Space from Monocular Videos Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. Existing methods face challenges in achieving detailed 3D tracking because they often track only a few points, which need more detail for full-scene understanding. They also demand computational power, making it difficult to handle long videos efficiently. Additionally, many of them must be fixed to maintain accuracy over extended sequences, as problems like camera movement and object occlusion cause the model to lose track or introduce errors. Read the full article: https://lnkd.in/dQJRgESz Paper: https://lnkd.in/dUyP4Nxw

🔑 Unlock the power of Model-Based Design! 🔑 👉 Join us at #MATLABEXPO to explore tools for modeling dynamic systems using first principles, grey-box, and data-driven modeling including AI techniques. #MATLAB #Simulink #Simscape #Engineering #AI https://spr.ly/6041S5BNz

🚀 𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗜𝗺𝗮𝗴𝗲 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗠𝗮𝘀𝗸 𝗥-𝗖𝗡𝗡! 📸 In my last post, I explored the U-Net model for semantic segmentation. Today, I want to shift focus to another powerful technique in image segmentation: 𝗶𝗻𝘀𝘁𝗮𝗻𝗰𝗲 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘂𝘀𝗶𝗻𝗴 𝗠𝗮𝘀𝗸 𝗥-𝗖𝗡𝗡! 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗮𝘀𝗸 𝗥-𝗖𝗡𝗡? 🤔 Mask R-CNN builds on the foundations of Faster R-CNN, adding a critical layer: the ability to generate high-quality segmentation masks for each detected object. This means not only can we identify and localize objects in an image, but we can also outline their precise shapes! 𝗪𝗵𝘆 𝗗𝗼𝗲𝘀 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿? 🌟 Enhanced Object Detection: By understanding the exact boundaries of objects, applications in autonomous driving, medical imaging, and robotics become significantly more accurate. Versatile Applications: From image editing to complex scene understanding, the possibilities are endless. Whether it’s analyzing satellite imagery or improving augmented reality experiences, Mask R-CNN is at the forefront. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 𝗥𝗲𝗴𝗶𝗼𝗻 𝗣𝗿𝗼𝗽𝗼𝘀𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗥𝗣𝗡): Efficiently identifies regions of interest in an image. 𝗙𝘂𝗹𝗹𝘆 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗙𝗖𝗡): Produces pixel-wise segmentation masks, allowing for precise object delineation. 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: Streamlined workflow that improves performance across the board. As we continue to explore the capabilities of AI and machine learning, tools like Mask R-CNN are proving to be invaluable. They not only push the boundaries of what’s possible in image analysis but also inspire innovation across industries. 🔍If you’re interested in learning, check out my GitHub:https://lnkd.in/g8-9E2jf Let’s connect and share insights! 🌟 #ComputerVision #ImageSegmentation #MaskRCNN #AI #MachineLearning #DeepLearning #Innovation