In this section, we describe our data generation procedure for training and rendering the object scans from OmniObject3D

To test the out-of-domain generalization ability, we generate images of imaginary objects as the input to our model (see Fig. 10).

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We show additional qualitative results on OmniObject3D, Ocrtoc3D, and Pix3D in Fig. 7, Fig. 8 and Fig. 9, respectively.

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One significant gap we noticed was that Home Assistant doesn't automatically adjust the brightness according to the time of the day.

Are you ready to level up your skills as a developer in 2025? In this article, I show 10 books that will help you become a better programmer, enhance your coding practices, and improve your overall software engineering skills. Whether you’re a beginner or…

We evaluate the quality of the reconstructed geometry of different methods. The quantitative results are summarized in Table 1.

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In this paper, we present Wonder3D, an innovative approach designed for efficiently generating high10 fidelity textured meshes from single-view images.

In this section, we conduct a set of studies to verify the effectiveness of our designs as well as the properties of the method.

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To strengthen the protocol's reliability, Xenea follows a peer-review approach. Core architecture papers are submitted to the IEEE, a prominent academic society

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From Peaks to Plains

Managing my own expectations has always been a battle. More times than I care to remember, I try to do it all. I try to add another item to…