9 papers
ATSplat: Compact Feed-forward 3D Gaussian Splatting with Adaptive Token Expansion
In Cho, Jeonghwan Cho, Mijin Yoo +2
3D Gaussian Splatting (3DGS) achieves high-quality novel-view synthesis by optimizing freely placed primitives in 3D and adaptively densifying them in under-reconstructed regions.…
Scenes as Objects, Not Primitives: Instance-Structured 3D Tokenization from Unposed Views
Mijin Yoo, In Cho, Subin Jeon +3
A 3D scene is understood through its objects, not the primitives that compose them. Yet feed-forward reconstruction methods output dense, unstructured sets of points or Gaussians,…
Seam360GS: Seamless 360° Gaussian Splatting from Real-World Omnidirectional Images
Changha Shin, Woong Oh Cho, Seon Joo Kim
360-degree visual content is widely shared on platforms such as YouTube and plays a central role in virtual reality, robotics, and autonomous navigation. However, consumer-grade du…
ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors
Minsu Kim, Subin Jeon, In Cho +2
Recent advances in novel view synthesis (NVS) have enabled real-time rendering with 3D Gaussian Splatting (3DGS). However, existing methods struggle with artifacts and missing regi…
Unsupervised Monocular 3D Keypoint Discovery from Multi-View Diffusion Priors
Subin Jeon, In Cho, Junyoung Hong +2
Most existing 3D keypoint estimation methods rely on manual annotations or calibrated multi-view images, both of which are expensive to collect. This paper introduces KeyDiff3D, a…
Representing 3D Shapes With 64 Latent Vectors for 3D Diffusion Models
In Cho, Youngbeom Yoo, Subin Jeon +1
Constructing a compressed latent space through a variational autoencoder (VAE) is the key for efficient 3D diffusion models. This paper introduces COD-VAE that encodes 3D shapes in…
4D Scaffold Gaussian Splatting with Dynamic-Aware Anchor Growing for Efficient and High-Fidelity Dynamic Scene Reconstruction
Woong Oh Cho, In Cho, Seoha Kim +3
Modeling dynamic scenes through 4D Gaussians offers high visual fidelity and fast rendering speeds, but comes with significant storage overhead. Recent approaches mitigate this cos…
Hierarchically Structured Neural Bones for Reconstructing Animatable Objects from Casual Videos
Subin Jeon, In Cho, Minsu Kim +2
We propose a new framework for creating and easily manipulating 3D models of arbitrary objects using casually captured videos. Our core ingredient is a novel hierarchy deformation…
Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging
In Cho, Hyunbo Shim, Seon Joo Kim
This paper aims to facilitate more practical NLOS imaging by reducing the number of samplings and scan areas. To this end, we introduce a phasor-based enhancement network that is c…