NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis · HackerPapers discussions

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by nomadar

Fri, 15 May 2026 21:00:20 GMT

Curious about the actual architecture. From the outside it looks like Gaussian splatting anchored to roughly one viewpoint, since the moment you wander outside the original frame or behind an object, it becomes messy. But Ben Mildenhall is one of the co-founders and a NeRF co-author (https://arxiv.org/abs/2003.08934), so I'm betting that whatever they're doing is more interesting than naive splatting. Curious if OP can share anything about the pipeline.

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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by gwern

Fri, 10 Oct 2025 19:56:07 GMT

This apparently doesn't apply here, but in fact, pixels can be generated independently of each other. There are architectures where you can generate an arbitrary pixel or element of the image without generating the others; they are just implicit. See NeRFs or 'single-pixel GANs' or MAEs: eg https://arxiv.org/abs/2003.08934 https://arxiv.org/abs/2011.13775 https://arxiv.org/abs/2401.14391 Why is this possible? I tend to think of it as reflecting the ability to 'memorize' all possible data, and the independent generation is just when you 'remember' a specific part of a memory. The latent space…

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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by gamegoblin

Wed, 26 Apr 2023 20:15:34 GMT

NeRF was already standardized in the literature by people who are not these authors [0] and HDR is also already standardized by people who are not these authors [1]. Go with the flow unless you have a very good reason not to. HDR is probably HDR and not some other clever name because there was no obvious clever name with an easy pronunciation. Whereas NRF is just so close to NeRF -- an actual word people are familiar with -- it's begging to be pronounced that way. [0] https://arxiv.org/abs/2003.08934 [1] https://en.wikipedia.org/wiki/High_dynamic_range

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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by yalue

Thu, 06 Oct 2022 00:21:24 GMT

At least in the original paper [1], the key idea is that instead of training a neural network to treat "images" as an array of pixels, you train a network to map a camera location and direction (in 3D space) to a distance and a color. For example, if you tell a NeRF that you have a camera at location (x, y, z) and pointing in direction (g, h, j), then the network will output the distance at which the ray emitted from the camera is expected to "hit" something and the RGB color of whatever is hit. Doing things in this way enables rendering images at arbitrary resolutions (though rendering can…

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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by visarga

Fri, 30 Sep 2022 04:00:32 GMT

... that one has 52K citations, and the 2D to 3D paper "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis" with 1488 citations. https://arxiv.org/abs/2003.08934

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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by ath92

Sun, 16 Jan 2022 20:51:38 GMT

For some additional context, when the original NeRF paper (https://arxiv.org/pdf/2003.08934.pdf) was published 2 years ago, it reportedly took at least 12 hours (depending on hardware used of course) to train on the scene with the bulldozer. This has now been reduced to about 5 seconds (!), with realtime rendering of the result.

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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by wokwokwok

Fri, 02 Jul 2021 00:29:01 GMT

> If you're asking me for an example of it being publicly used in production, well I think you're asking a lot considering the technique is only a few months old That is what I explicitly asked for. You’re failure to provide an example is not because it’s new it’s because it’s actually not useful practically at the moment. NeRF has been around since March 2020 (https://arxiv.org/abs/2003.08934); you are simply wrong; traditional techniques are better right now, have better implementations and are widely used. NeRF is a promising technology that is categorically worse in its current…

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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — mentioned by srcreigh

Thu, 01 Jul 2021 00:54:18 GMT

I'm no data scientist but many statistical methods rely on prior knowledge and even computed inputs. Two examples I can think of are doing linear regression on the square of your input. For deep learning, people have improved visual representation by taking samples of the colors at various frequencies. [1] [1]: https://arxiv.org/pdf/2003.08934.pdf

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