<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>fxtentacle</author><text>They likely focus on the 1 image case because there it&amp;#x27;s the easiest to show visual progress over the competition. If you have 50+ images, the tech from 10 years ago is already good enough to get Hollywood-quality 3D scans.&lt;p&gt;From what I understand, they use the text keywords detected from the image as guidance and they also apply a loss between the current diffusion state and the source image. In effect, this is stable diffusion for 3D shapes but with clever conditioning. That means this algorithm will also work just fine if you have 2+ input images.</text><parent_chain><item><author>Geee</author><text>Why are researchers focusing on single-image reconstruction? It seems like a party trick which isn&amp;#x27;t very useful, and pretty much impossible to reconstruct the original object accurately. It would be much more useful if many images from different angles could be used. Somewhat like NERF, but also predict missing views with 2D diffusion. Adding more images would get the model closer to ground truth.</text></item></parent_chain></comment><story><title>Magic123: One Image to High-Quality 3D Object Generation</title><url>https://guochengqian.github.io/project/magic123/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>vlovich123</author><text>Because there’s people working on all sorts of different problems and solving a problem in one area can apply better to some problems than others. Not to mention that solution approaches can often cross pollinate.&lt;p&gt;Research is additive not a zero sum thing.</text><parent_chain><item><author>Geee</author><text>Why are researchers focusing on single-image reconstruction? It seems like a party trick which isn&amp;#x27;t very useful, and pretty much impossible to reconstruct the original object accurately. It would be much more useful if many images from different angles could be used. Somewhat like NERF, but also predict missing views with 2D diffusion. Adding more images would get the model closer to ground truth.</text></item></parent_chain></comment><story><title>Magic123: One Image to High-Quality 3D Object Generation</title><url>https://guochengqian.github.io/project/magic123/</url></story>