paper-with-me

Papers

Complex-Valued Holographic Radiance Fields

2025-06-10 · Yicheng Zhan, Dong-Ha Shin, Seung-Hwan Baek, Kaan Akşit

Modeling the full properties of light, including both amplitude and phase, in 3D representations is crucial for advancing physically plausible rendering, particularly in holographic displays. To support these features, we propose a novel representation that optimizes 3D scenes without relying on intensity-based intermediaries. We reformulate 3D Gaussian splatting with complex-valued Gaussian primitives, expanding support for rendering with light waves. By leveraging RGBD multi-view images, our method directly optimizes complex-valued Gaussians as a 3D holographic scene representation. This eliminates the need for computationally expensive hologram re-optimization. Compared with state-of-the-art methods, our method achieves 30x-10,000x speed improvements while maintaining on-par image quality, representing a first step towards geometrically aligned, physically plausible holographic scene representations.

📄 PDF Abstract BibTeX arXiv:2506.08350

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

Similar Papers 제목 키워드 기반

Complex-Valued 2D Gaussian Representation for Computer-Generated Holography

2025-11-19 · Yicheng Zhan, Xiangjun Gao, Long Quan, Kaan Akşit arxiv

Complex-valued Gaussian primitives have recently been explored for representing holographic radiance fields in 3D novel view synthesis. In this work, we extend this line of research to the hologram optimization domain an…

Novel View Synthesis

Holographic Transformers for Complex-Valued Signal Processing: Integrating Phase Interference into Self-Attention

2025-09-14 · Enhao Huang, Zhiyu Zhang, Tianxiang Xu, Chunshu Xia 외 arxiv

Complex-valued signals encode both amplitude and phase, yet most deep models treat attention as real-valued correlation, overlooking interference effects. We introduce the Holographic Transformer, a physics-inspired arch…

Image Classification

Learned holographic light transport

2021-08-01 · Koray Kavaklı, Hakan Urey, Kaan Akşit

Computer-Generated Holography (CGH) algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in ho…

Associative Long Short-Term Memory

2016-02-09 · Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner 외

We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters. The system has an associative memory based on complex-valued vectors and is closely …

MemorizationRetrieval

VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment

2023-12-07 · CVPR 2024 1 · Phong Tran, Egor Zakharov, Long-Nhat Ho, Anh Tuan Tran 외

We present a 3D-aware one-shot head reenactment method based on a fully volumetric neural disentanglement framework for source appearance and driver expressions. Our method is real-time and produces high-fidelity and vie…

DisentanglementSelf-Supervised Learning