3 papers
cs.CV2024
EucliDreamer: Fast and High-Quality Texturing for 3D Models with Depth-Conditioned Stable Diffusion
Cindy Le, Congrui Hetang, Chendi Lin +2
We present EucliDreamer, a simple and effective method to generate textures for 3D models given text prompts and meshes. The texture is parametrized as an implicit function on the…
cs.CV2024
Segment Anything Model for Road Network Graph Extraction
Congrui Hetang, Haoru Xue, Cindy Le +3
We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) for extracting large-scale, vectorized road network graphs from satellite imagery. To predict graph geometry,…
cs.CV2024
EucliDreamer: Fast and High-Quality Texturing for 3D Models with Stable Diffusion Depth
Cindy Le, Congrui Hetang, Chendi Lin +2
This paper presents a novel method to generate textures for 3D models given text prompts and 3D meshes. Additional depth information is taken into account to perform the Score Dist…