Nicole Feng
@nicolefeng_
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PhD student @SCSatCMU, @GeomCollective
United States
Joined May 2019
I enjoyed - and really benefited from! - my experience with WiGRAPH last year. Consider applying or helping out!
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RT @JustinMSolomon: Announcing SGI 2025! Undergrads and MS students: Apply for 6 weeks of paid summer geometry processing research. No expe…
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It can be hard to explain to non-specialists just how deep & high-quality/impact Mark's projects are (if his pretty website & high-quality code are any indication). Mark can also be frustratingly humble. Just have a conversation with him and you'll get it. [2/2]
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RT @docmilanfar: How are Kernel Smoothing in statistics, Data-Adaptive Filters in image processing, and Attention in Machine Learning relat…
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@anisomorphism Theory: heat kernel behavior <=> stochastic processes, geodesics, transport, curvature... Algorithms: heat methods for geodesic distance, diffusion maps, functional maps, spectral methods for shape analysis more generally, etc. And neural variants used in e.g. manifold learning.
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A similar story has played out in some geometry processing algorithms, including our recent paper on robust SDFs: Saw Iolo's work at SoCG, and looking forward to taking a deeper dive into this new paper.
Signed distance functions (SDFs) are fundamental tools in graphics, vision, and physics simulation. But how do you get a high-quality SDF from messy, real-world input? At #SIGGRAPH2024, we introduced a simple method for turning "broken" geometry into a well-behaved SDF. <🧵>
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RT @TianchangS: Generating nice meshes in AI pipelines is hard. Our #SIGGRAPHAsia2024 paper proposes a new representation which guarantees…
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@amirvaxman_dgp @yotam_erel The SHM also requires your points to be (mostly) oriented, not sure yet how to best extend to unoriented data. Could use the shrink-wrapping methods Bruno mentioned, which should work well for densely sampled point clouds that approximate your surfaces well.
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RT @GCM_EPFL: Please share: Our lab is looking for new PhD students starting 2025! If you are interested in research in geometry, computat…
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RT @JustinMSolomon: My group will be seeking new PhD students in the coming cycle! The best way to reach us is to apply to the @MITEECS PhD…
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@BattleAxeVR You could pre-compute some "simplified" surfaces along the lines of this example. Not entirely sure yet how to do this "on the fly"/continuously, but working on it. Maybe something like Hoppe's "progressive meshes" is more appropriate?
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@danbri Haven't tried gaussian splats, but in principle the method should work as you have normals for the geometry - which for gaussians might be done by taking a gradient where you've deemed your zero level set surface to be. Might be some finicky details to figure out.
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FYI shoutout to Chris Yu and Nick Sharp (@nmwsharp) for developing the awesome volume grid/tet visualization features in Polyscope.
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Main X thread here: Paper and project here:
Signed distance functions (SDFs) are fundamental tools in graphics, vision, and physics simulation. But how do you get a high-quality SDF from messy, real-world input? At #SIGGRAPH2024, we introduced a simple method for turning "broken" geometry into a well-behaved SDF. <🧵>
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Code has been added to geometry-central (! Demo projects here:
Signed distance functions (SDFs) are fundamental tools in graphics, vision, and physics simulation. But how do you get a high-quality SDF from messy, real-world input? At #SIGGRAPH2024, we introduced a simple method for turning "broken" geometry into a well-behaved SDF. <🧵>
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