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Ziteng Cui

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4 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Dr. RAW: Towards General High-Level Vision from RAW with Efficient Task Conditioning

Wenjun Huang
Ziteng Cui
Yinqiang Zheng
Yirui He
Tatsuya Harada
Mohsen Imani

We introduce Dr. RAW, a unified and tuning-efficient framework for high-level computer vision tasks directly operating on camera RAW data. Unlike previous approaches that optimize image signal processing (ISP) pipelines and fully fine-tune networks for each task, Dr. RAW achieves state-of-the-art performance with minimal parameter updates. At the input stage, we apply lightweight pre-processing modules, sensor and illumination mapping, followed by re-mosaicing, to mitigate data inconsistencies stemming from sensor variation and lighting. At the network level, we introduce task-specific adaptation through two modules: Sensor Prior Prompts (SPP) and Low-Rank Adaptation (LoRA). SPP injects sensor-aware conditioning into the network via learnable prompts derived from imaging priors, while LoRA enables efficient task-specific tuning by updating only low-rank matrices in key backbone layers. Despite minimal tuning, our method delivers superior results across four RAW-based tasks (object detection, semantic segmentation, instance segmentation, and pose estimation) on nine datasets encompassing low-light and over-exposed conditions. By harnessing the intrinsic physical cues of RAW data alongside parameter-efficient techniques, our method advances RAW-based vision systems, achieving both high accuracy and computational economy. We will release our source code.

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TMLR Journal 2025 Journal Article

Enhancing Plaque Segmentation in CCTA with Prompt- based Diffusion Data Augmentation

Ruan Yizhe
Xuangeng Chu
Ziteng Cui
Yusuke Kurose
JUNICHI IHO
Yoji Tokunaga
Makoto Horie
YUSAKU HAYASHI

Coronary computed tomography angiography (CCTA) is essential for non-invasive assessment of coronary artery disease (CAD). However, accurate segmentation of atherosclerotic plaques remains challenging due to data scarcity, severe class imbalance, and significant variability between calcified and non-calcified plaques. Inspired by DiffTumor’s tumor synthesis and PromptIR’s adaptive restoration framework, we introduce PromptLesion, a prompt-conditioned diffusion model for multi-class lesion synthesis. Unlike single-class methods, our approach integrates lesion-specific prompts within the diffusion generation process, enhancing diversity and anatomical realism in synthetic data. We validate PromptLesion on a private CCTA dataset and multi-organ tumor segmentation tasks (kidney, liver, pancreas) using public datasets, achieving superior performance compared to baseline methods. Models trained with our prompt-guided synthetic augmentation significantly improve Dice Similarity Coefficient (DSC) scores for both plaque and tumor segmentation. Extensive evaluations and ablation studies confirm the effectiveness of prompt conditioning.

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NeurIPS Conference 2025 Conference Paper

I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions

Shuhong Liu
Lin Gu
Ziteng Cui
Xuangeng Chu
Tatsuya Harada

Participating in efforts to endow generative AI with the 3D physical world perception, we propose I2-NeRF, a novel neural radiance field framework that enhances isometric and isotropic metric perception under media degradation. While existing NeRF models predominantly rely on object-centric sampling, I2-NeRF introduces a reverse-stratified upsampling strategy to achieve near-uniform sampling across 3D space, thereby preserving isometry. We further present a general radiative formulation for media degradation that unifies emission, absorption, and scattering into a particle model governed by the Beer–Lambert attenuation law. By matting direct and media-induced in-scatter radiance, this formulation extends naturally to complex media environments such as underwater, haze, and even low-light scenes. By treating light propagation uniformly in both vertical and horizontal directions, I2-NeRF enables isotropic metric perception and can even estimate medium properties such as water depth. Experiments on real-world datasets demonstrate that our method significantly improves both reconstruction fidelity and physical plausibility compared to existing approaches. The source code is available at https: //github. com/ShuhongLL/I2-NeRF.

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AAAI Conference 2024 Conference Paper

Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption

Ziteng Cui
Lin Gu
Xiao Sun
Xianzheng Ma
Yu Qiao
Tatsuya Harada

The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered methodology, entangling the aspects of illumination and material reflectance into emission solely from 3D points. This simplified rendering approach presents challenges in accurately modeling images captured under adverse lighting conditions, such as low light or over-exposure. Motivated by the ancient Greek emission theory that posits visual perception as a result of rays emanating from the eyes, we slightly refine the conventional NeRF framework to train NeRF under challenging light conditions and generate normal-light condition novel views unsupervisedly. We introduce the concept of a ``Concealing Field," which assigns transmittance values to the surrounding air to account for illumination effects. In dark scenarios, we assume that object emissions maintain a standard lighting level but are attenuated as they traverse the air during the rendering process. Concealing Field thus compel NeRF to learn reasonable density and colour estimations for objects even in dimly lit situations. Similarly, the Concealing Field can mitigate over-exposed emissions during rendering stage. Furthermore, we present a comprehensive multi-view dataset captured under challenging illumination conditions for evaluation. Our code and proposed dataset are available at https://github.com/cuiziteng/Aleth-NeRF.

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