activity
20232025
most citedSegment Anything Model for Road Network Graph Extraction

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Objaverse++: Curated 3D Object Dataset with Quality Annotations

Chendi Lin, Heshan Liu, Qunshu Lin +6

This paper presents Objaverse++, a curated subset of Objaverse enhanced with detailed attribute annotations by human experts. Recent advances in 3D content generation have been dri…

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★ 1 cited

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.CV2023

Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers

Hai Phan, Cindy Le, Vu Le +2

Most face identification approaches employ a Siamese neural network to compare two images at the image embedding level. Yet, this technique can be subject to occlusion (e.g. faces…

cs.CV2023★ 1 cited

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…