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SAP: Exact Sorting in Splatting via Screen-Aligned Primitives Contributions: - We introduce a 3D-consistent decoder, enabling our framework to be constructed on a 3D feature network combined with 2D reconstruction primitives. - We propose the Screen-Aligned Primitives (SAP) representation for more accurate sorting. - We demonstrate that our framework supports accurate and unbiased projection, allowing for the use of more flexible and expressive kernel representations. - We present a densification strategy based on the maximum positional gradient, increasing point density in regions with significant view-dependent variation, thereby enhancing reconstruction quality.
Paper (pdf): https://t.co/QUZzoo3F2E
I love customer success stories on the official Laravel blog: https://t.co/kE4jIciqbR Sure, they are "too polished", look almost like ads imho, but they feature REAL companies and brands. For Laravel spread, such stories are more valuable than random tweets here and there.
~20 yrs in web-dev, now mostly Laravel. My Laravel courses: https://t.co/HRUAJdMRZL My Youtube channel: https://t.co/qPQAkaov2F
Paper (pdf): https://t.co/YLwEAEIDqS
Your guide to radiance fields | Host of the podcast @ViewDependent | Founder and CEO of https://t.co/5MjtfpwEU3 | FTP: 279 | discord: https://t.co/lrl64WGvlD
Deep Gaussian from Motion: Exploring 3D Geometric Foundation Models for Gaussian Splatting Contributions: • Pose-free Foundation Model Adaptation: Unlike VGGT/MegaSAM, which rely on pre-computed poses (potentially affected by inaccuracies), our pipeline operates without pose annotations. This is achieved by refining Gaussian geometries dynamically to align photometric appearance with ray-consistent novel view synthesis. • Progressive and Modular Framework Design: The progressive design enables iterative scalability, addressing GPU bottlenecks evident in VGGT-like pipelines. Modularity ensures robustness against scene diversity, allowing refinement of components independent of memory constraints imposed by dense image sets. • Scene-specific Gaussian Prediction: Our method dynamically predicts Gaussian geometries for each input scene, adapting to its unique photometric and geometric characteristics for high-quality synthesis—a flexibility less evident in feed-forward methods like VGGT/MegaSAM, which process inputs less adaptively.
Paper (pdf): https://t.co/YLwEAEIDqS
RT @NiohBerg: NYC will go in the same direction as London. Things will get progressively worse, dirtier and more dangerous. Everything will…
AI Optimist. Empiricist, not 'rationalist'. Anti world government.
https://t.co/cI60bXuVAz
Believing is seeing