The technological breakthrough of TripoSG is mainly reflected in its innovative training method. The system adopts a hybrid supervised training strategy, combining both explicit geometric supervision and implicit representation learning paradigms, so that the model can accurately capture macroscopic structural features as well as restore microscopic surface details. At the data processing level, the R&D team constructed a high-quality dataset containing more than 1 million image-3D model pairs, covering multiple types of samples ranging from real objects to artistic creations. Tests show that the average vertex accuracy of the mesh model generated by this method is 42% higher than that of the traditional SIFR method, especially when dealing with complex surfaces, it can maintain a more complete topology and avoid the holes and distortions that are common in traditional methods.
This answer comes from the articleTripoSG: Generating high-resolution 3D modeled digital assets from a single imageThe
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