It’s grad school application season, and I wanted to give some public advice.
Caveats:
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> These are my opinions, based on my experiences, they are not secret tricks or guarantees
> They are general guidelines, not meant to cover a host of idiosyncrasies and special cases
We are trying to create a list of in-copyright novels that contain maps. If you know of some, drop them in the thread below! 🧵👇
Are fictional maps okay? If yes, the inheritance cycle by Christopher paolini, also the Throne of Glass series by Sarah J Maas
For those at CVPR, @justachetan.bsky.social will be presenting this poster tomorrow at 10:30 (Exhibit hall D, Poster #34). Come hear about why neural field derivatives are noisy, and how we resurrect image processing ideas for neural fields!
Thrilled to attend my first-ever #CVPR2025! 🎉
Please reach out if you would like to chat about neural fields, dynamic scenes, video understanding, or just generally about gaming, musicals, or ☕️
I will also be presenting our poster ⬇️ (Come visit!)
Happy to get feedback + questions! For more experiments and technical details, check out our paper! 😄
We also show improved performance in downstream applications like rendering, collision simulation, and PDE solving.
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We show the effectiveness of our method in computing accurate normals and curvatures over a variety of challenging neural SDFs learned over the FamousShape dataset. Our approach shows a 4x improvement in gradients and mean curvature over the baselines.
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Second, to enable smoother gradients directly with autodiff over the network, we propose a fine-tuning approach that can use any smooth gradient operator to smooth out the artifacts in the gradients.
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To mitigate this noise, we propose a two-pronged solution. First, we leverage the classical technique of polynomial-fitting to fit low-order polynomials through the learned signal and take autodiff over the fitted polynomial.
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What causes these artifacts? We note that signals learned by hybrid neural fields exhibit high-frequency noise (see FFT of a 1D slice of a 2D SDF), which gets amplified when we take derivatives using standard tools like autodiff.
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Hybrid neural fields like Instant NGP have made training neural fields extremely efficient. However, we find that they fall short of being "faithful" representations, exhibiting noisy artifacts when we compute their spatial derivatives with autodiff.
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Check out our poster at #CVPR2025 on accurate differential operators for hybrid neural fields (like Instant NGP)!
🗓️ Fri, June 13, 10:30 AM–12:30 PM
📍 ExHall D, Poster #34
🔗 justachetan.github.io/hnf-derivati...
👉 cvpr.thecvf.com/virtual/2025...
Details ⬇️ (1/n)
Reasoning about the "why" behind user behavior can improve LLM personas! ✨🧠📈
📝Excited to share our new work: Improving LLM Personas via Rationalization with Psychological Scaffolds
🔗 arxiv.org/abs/2504.17993
🧵 (1/n)
[1/10] Is scene understanding solved?
Models today can label pixels and detect objects with high accuracy. But does that mean they truly understand scenes?
Super excited to share our new paper and a new task in computer vision: Visual Jenga!
📄 arxiv.org/abs/2503.21770
🔗 visualjenga.github.io
Introducing MegaSaM!
Accurate, fast, & robust structure + camera estimation from casual monocular videos of dynamic scenes!
MegaSaM outputs camera parameters and consistent video depth, scaling to long videos with unconstrained camera paths and complex scene dynamics!
