NeRFs are sort of last year's technology. The latest hype is about gaussian splats. My understanding is that essentially these technologies take some images as

NeRFs are sort of last year's technology. The latest hype is about gaussian splats. My understanding is that essentially these technologies take some images as input, and then train a model, where the model is learning the best way to render the imagines into a model in some sense. I think for gaussian splats, it represents images as sort of "blobs" in space, and each image has the same set of blobs that have to be used from some perspective to render the image, hence by positioning the splats such that each image is rendered correctly, you can reproduce the scene. This training is currently very expensive and has to be done for each model, but produces an output that can be explored in real time. I think the photogrammetry approaches used by matterport et all are older and require much higher quality input data, whereas the newer approaches can work with much less and lower quality data.