most citedMeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers

8 citations · 22 across the 13 of their papers we have counts for

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cs.CV2024★ 1 cited

CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction

Yuanyuan Gao, Yalun Dai, Hao Li +7

3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction. However, most existing GS-based surface reconstruction methods focus on 3D objects or…

cs.CV2024★ 2 cited

DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion

Weicai Ye, Chenhao Ji, Zheng Chen +7

Diffusion-based methods have achieved remarkable achievements in 2D image or 3D object generation, however, the generation of 3D scenes and even images remains constr…

cs.CV2024

Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction

Junyi Chen, Di Huang, Weicai Ye +2

Spatial intelligence is the ability of a machine to perceive, reason, and act in three dimensions within space and time. Recent advancements in large-scale auto-regressive models h…

cs.CV2024★ 1 cited

StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting

Xiao Cui, Weicai Ye, Yifan Wang +3

Reconstructing urban street scenes is crucial due to its vital role in applications such as autonomous driving and urban planning. These scenes are characterized by long and narrow…

cs.CV2024★ 2 cited

HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction

Shengji Tang, Weicai Ye, Peng Ye +4

Reconstructing 3D scenes from multiple viewpoints is a fundamental task in stereo vision. Recently, advances in generalizable 3D Gaussian Splatting have enabled high-quality novel…

cs.CV2024★ 1 cited

GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction

Junyi Chen, Weicai Ye, Yifan Wang +6

3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. Previous methods adapt it to obtaining surfaces of either individual 3D objects or within limi…

cs.CV2024★ 1 cited

DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting

Weiwei Cai, Weicai Ye, Peng Ye +2

Dynamic scene reconstruction has garnered significant attention in recent years due to its capabilities in high-quality and real-time rendering. Among various methodologies, constr…

cs.CV2024

ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction

Ziyu Tang, Weicai Ye, Yifan Wang +4

Neural implicit reconstruction via volume rendering has demonstrated its effectiveness in recovering dense 3D surfaces. However, it is non-trivial to simultaneously recover meticul…

cs.CV2024

NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction

Yifan Wang, Di Huang, Weicai Ye +3

Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction. Although promising, SDF-based methods often fail to captu…

cs.CV2024★ 1 cited

Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers

Peng Gao, Le Zhuo, Dongyang Liu +17

Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lac…

cs.CV2024★ 8 cited

MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers

Yiwen Chen, Tong He, Di Huang +9

Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this poten…

cs.CV2024★ 1 cited

NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection

Chenxi Huang, Yuenan Hou, Weicai Ye +5

NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by innovatively utilizing NeRF to enhance representation learning. Despite its notable performance, w…