Map2World produces scale-consistent 3D worlds from text and arbitrary segment maps via a detail enhancer that incorporates global structure information.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Rein3D generates photorealistic, globally consistent 3D indoor scenes by using a restore-and-refine process where radial panoramic videos are restored via diffusion models and then used to update a 3D Gaussian field.
PAD synthesizes 3D geometry in observation space via depth unprojection as anchor to eliminate pose ambiguity in image-to-3D generation.
citing papers explorer
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Map2World: Segment Map Conditioned Text to 3D World Generation
Map2World produces scale-consistent 3D worlds from text and arbitrary segment maps via a detail enhancer that incorporates global structure information.
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Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models
Rein3D generates photorealistic, globally consistent 3D indoor scenes by using a restore-and-refine process where radial panoramic videos are restored via diffusion models and then used to update a 3D Gaussian field.
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Pose-Aware Diffusion for 3D Generation
PAD synthesizes 3D geometry in observation space via depth unprojection as anchor to eliminate pose ambiguity in image-to-3D generation.