用扩散模型生成更真实的高动态环境图，解决传统方法的极区失真问题。
HDR Environment Map Estimation with Latent Diffusion Models
- 基于潜在扩散模型，结合全景适应的Transformer架构提升环境图质量。
- 提出潜空间卷积填充方法消除等距柱状投影的接缝伪影。
- 在标准基准上表现媲美顶尖方法，适合虚拟现实与真实光照渲染场景。
我们通过引入潜在扩散模型（LDM），提出一种从单视角图像生成高质量高动态环境图的新方法，可真实还原镜面反射表面的光照效果。现有主流方法多采用等距柱状投影（ERP）表示，常导致极区畸变和侧边接缝伪影。为此，我们设计了一种潜空间卷积填充策略，有效消除接缝缺陷。同时，提出全景自适应扩散变换器（PanoDiT），以适配ERP格式，减少畸变，但牺牲了部分图像质量和真实感。在标准基准测试中，所提模型在图像质量与光照准确性上均达到当前最优水平，性能优于或相当主流方法。
原文摘要 · Abstract (English)
We advance the field of HDR environment map estimation from a single-view image by establishing a novel approach leveraging the Latent Diffusion Model (LDM) to produce high-quality environment maps that can plausibly light mirror-reflective surfaces. A common issue when using the ERP representation, the format used by the vast majority of approaches, is distortions at the poles and a seam at the sides of the environment map. We remove the border seam artefact by proposing an ERP convolutional padding in the latent autoencoder. Additionally, we investigate whether adapting the diffusion network architecture to the ERP format can improve the quality and accuracy of the estimated environment map by proposing a panoramically-adapted Diffusion Transformer architecture. Our proposed PanoDiT network reduces ERP distortions and artefacts, but at the cost of image quality and plausibility. We evaluate with standard benchmarks to demonstrate that our models estimate high-quality environment maps that perform competitively with state-of-the-art approaches in both image quality and lighting accuracy.
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