19 citations · 59 across the 13 of their papers we have counts for
13 papers · 1 filter
TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields
Haoxuan Li, Ziya Erkoç, Daniele Sirigatti +4
We present TriFlow, a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fie…
Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow
Quan Meng, Yujin Chen, Lei Li +2
We present Seen2Scene, the first flow matching-based approach that trains directly on incomplete, real-world 3D scans for scene completion and generation. Unlike prior methods that…
MeshArt: Generating Articulated Meshes with Structure-Guided Transformers
Daoyi Gao, Yawar Siddiqui, Lei Li +1
Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchica…
LT3SD: Latent Trees for 3D Scene Diffusion
Quan Meng, Lei Li, Matthias Nießner +1
We present LT3SD, a novel latent diffusion model for large-scale 3D scene generation. Recent advances in diffusion models have shown impressive results in 3D object generation, but…
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning
Souhail Hadgi, Lei Li, Maks Ovsjanikov
Transfer learning has long been a key factor in the advancement of many fields including 2D image analysis. Unfortunately, its applicability in 3D data processing has been relative…
GenZI: Zero-Shot 3D Human-Scene Interaction Generation
Lei Li, Angela Dai
Can we synthesize 3D humans interacting with scenes without learning from any 3D human-scene interaction data? We propose GenZI, the first zero-shot approach to generating 3D human…
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
Lei Li, Nicolas Donati, Maks Ovsjanikov
In this work, we present a novel non-rigid shape matching framework based on multi-resolution functional maps with spectral attention. Existing functional map learning methods all…
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence
Lei Li, Souhaib Attaiki, Maks Ovsjanikov
In this work, we present a novel learning-based framework that combines the local accuracy of contrastive learning with the global consistency of geometric approaches, for robust n…
WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration
Lei Li, Hongbo Fu, Maks Ovsjanikov
In this work, we present a novel method called WSDesc to learn 3D local descriptors in a weakly supervised manner for robust point cloud registration. Our work builds upon recent 3…
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Xuyang Bai, Zixin Luo, Lei Zhou +5
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning…
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
Lei Li, Siyu Zhu, Hongbo Fu +2
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-…
SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence
Deng Yu, Lei Li, Youyi Zheng +4
In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as ou…
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
Lei Li, Changqing Zou, Youyi Zheng +3
Freehand sketching is a dynamic process where points are sequentially sampled and grouped as strokes for sketch acquisition on electronic devices. To recognize a sketched object, m…