arXiv:2603.23297v2 Announce Type: replace-cross Abstract: Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a diverse set of distortion losses. We conduct the first-of-its-kind large-scale human subjective study on 3DGS, involving 39,320 pairwise ratings across several datasets and 3DGS frameworks. A regularized version of Wasserstein Distortion, which
The accelerating advancement of AI models capable of generating and manipulating complex 3D environments is pushing the boundaries of realism, making perceptual optimization a critical next step.
Improving the visual quality of 3D Gaussian Splatting via perceptual optimization enhances realism and user experience, which is crucial for applications across metaverse, generative AI, and simulation training.
This research introduces a systematic approach to perceptually optimize 3D Gaussian Splatting, moving beyond ad-hoc pixel-level losses to achieve more human-eye-pleasing results through advanced distortion metrics.
- · Metaverse platforms
- · Generative AI companies
- · Gaming industry
- · VR/AR hardware manufacturers
- · Traditional 3D rendering pipelines
- · Companies relying solely on pixel-level loss functions
Wider adoption and higher quality of 3D digital content for various applications due to more visually appealing renders.
Increased demand for computational resources and specialized hardware capable of handling complex perceptual optimization algorithms in real-time.
The blurring of lines between synthetic and real-world visual experiences, potentially impacting media authenticity and human perception.
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