3 citations · 3 across the 6 of their papers we have counts for
9 papers
Unravel: Recovering an Interactive 3D Scene from a Single Image
Hui Shan, Keyang Luo, Binjia Zhou +10
We present Unravel, a training-free framework that turns a single image into an interactive 3D scene, where every object is an independent and movable asset. Existing generators ei…
Scenix: Sparse-View 3D Scene Reconstruction via Executable Scene Programs
Kai Li, Lutao Jiang, Zhenyang Li +10
Synthesizing a structured and editable 3D indoor scene from a few uncalibrated RGB views requires more than generating high-quality individual assets: a system must infer the room…
MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction
Dehao Hao, Kaiyi Zhang, Tanghui Jia +10
High-fidelity 3D generative modeling increasingly relies on the latent diffusion paradigm, where the reconstruction quality of the underlying 3D VAE becomes a primary bottleneck. E…
SAP: Segment Any 4K Panorama
Lutao Jiang, Zidong Cao, Weikai Chen +14
Promptable instance segmentation is widely adopted in embodied and AR systems, yet the performance of foundation models trained on perspective imagery often degrades on 360° panora…
CanoVerse: 3D Object Scalable Canonicalization and Dataset for Generation and Pose
Li Jin, Yuchen Yang, Weikai Chen +11
3D learning systems implicitly assume that objects occupy a coherent reference frame. Nonetheless, in practice, every asset arrives with an arbitrary global rotation, and models ar…
CoSMo3D: Open-World Promptable 3D Semantic Part Segmentation through LLM-Guided Canonical Spatial Modeling
Li Jin, Weikai Chen, Yujie Wang +7
Open-world promptable 3D semantic segmentation remains brittle as semantics are inferred in the input sensor coordinates. Yet, humans, in contrast, interpret parts via functional r…
MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Agentic Post-Processing
Lingting Zhu, Jingrui Ye, Runze Zhang +8
Current methods for 3D generation still fall short in physically based rendering (PBR) texturing, primarily due to limited data and challenges in modeling multi-channel materials.…
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
Zeyu Hu, Xuyang Bai, Runze Zhang +4
We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained m…
VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation
Zeyu Hu, Xuyang Bai, Jiaxiang Shang +6
In recent years, sparse voxel-based methods have become the state-of-the-arts for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs. Nevertheless, being obl…