42-Issue 7

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https://diglib.eg.org/handle/10.2312/3543886

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Browsing 42-Issue 7 by Subject "based rendering"

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    CP-NeRF: Conditionally Parameterized Neural Radiance Fields for Cross-scene Novel View Synthesis
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) He, Hao; Liang, Yixun; Xiao, Shishi; Chen, Jierun; Chen, Yingcong; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Neural radiance fields (NeRF) have demonstrated a promising research direction for novel view synthesis. However, the existing approaches either require per-scene optimization that takes significant computation time or condition on local features which overlook the global context of images. To tackle this shortcoming, we propose the Conditionally Parameterized Neural Radiance Fields (CP-NeRF), a plug-in module that enables NeRF to leverage contextual information from different scales. Instead of optimizing the model parameters of NeRFs directly, we train a Feature Pyramid hyperNetwork (FPN) that extracts view-dependent global and local information from images within or across scenes to produce the model parameters. Our model can be trained end-to-end with standard photometric loss from NeRF. Extensive experiments demonstrate that our method can significantly boost the performance of NeRF, achieving state-of-the-art results in various benchmark datasets.
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    Neural Impostor: Editing Neural Radiance Fields with Explicit Shape Manipulation
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Liu, Ruiyang; Xiang, Jinxu; Zhao, Bowen; Zhang, Ran; Yu, Jingyi; Zheng, Changxi; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Neural Radiance Fields (NeRF) have significantly advanced the generation of highly realistic and expressive 3D scenes. However, the task of editing NeRF, particularly in terms of geometry modification, poses a significant challenge. This issue has obstructed NeRF's wider adoption across various applications. To tackle the problem of efficiently editing neural implicit fields, we introduce Neural Impostor, a hybrid representation incorporating an explicit tetrahedral mesh alongside a multigrid implicit field designated for each tetrahedron within the explicit mesh. Our framework bridges the explicit shape manipulation and the geometric editing of implicit fields by utilizing multigrid barycentric coordinate encoding, thus offering a pragmatic solution to deform, composite, and generate neural implicit fields while maintaining a complex volumetric appearance. Furthermore, we propose a comprehensive pipeline for editing neural implicit fields based on a set of explicit geometric editing operations. We show the robustness and adaptability of our system through diverse examples and experiments, including the editing of both synthetic objects and real captured data. Finally, we demonstrate the authoring process of a hybrid synthetic-captured object utilizing a variety of editing operations, underlining the transformative potential of Neural Impostor in the field of 3D content creation and manipulation.
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    Robust Distribution-aware Color Correction for Single-shot Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Dhillon, Daljit Singh J.; Joshi, Parisha; Baron, Jessica; Patterson, Eric K.; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Color correction for photographed images is an ill-posed problem. It is also a crucial initial step towards material acquisition for inverse rendering methods or pipelines. Several state-of-the-art methods rely on reducing color differences for imaged reference color chart blocks of known color values to devise or optimize their solution. In this paper, we first establish through simulations the limitation of this minimality criteria which in principle results in overfitting. Next, we study and propose a few spatial distribution measures to augment the evaluation criteria. Thereafter, we propose a novel patch-based, white-point centric approach that processes luminance and chrominance information separately to improve on the color matching task. We compare our method qualitatively with several state-of-the art methods using our augmented evaluation criteria along with quantitative examinations. Finally, we perform rigorous experiments and demonstrate results to clearly establish the benefits of our proposed method.