mlsanityData to 14 Oct 2026

What Makes for a Good Stereoscopic Image?

arXiv 2412.21127 ↗First posted 30 Dec 2024cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

With rapid advancements in virtual reality (VR) headsets, effectively measuring stereoscopic quality of experience (SQoE) has become essential for delivering immersive and comfortable 3D experiences. However, most existing stereo metrics focus on isolated aspects of the viewing experience such as visual discomfort or image quality, and have traditionally faced data limitations. To address these gaps, we present SCOPE (Stereoscopic COntent Preference Evaluation), a new dataset comprised of real and synthetic stereoscopic images featuring a wide range of common perceptual distortions and artifacts. The dataset is labeled with preference annotations collected on a VR headset, with our findings indicating a notable degree of consistency in user preferences across different headsets. Additionally, we present iSQoE, a new model for stereo quality of experience assessment trained on our dataset. We show that iSQoE aligns better with human preferences than existing methods when comparing mono-to-stereo conversion methods.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 38 of its 107 references that are in the map. First posted on arXiv 30 Dec 2024.

Builds on

38 of 107 references.

References · 38

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26 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Elastic3D: Controllable Stereo Video Conversion with Guided Latent Decodingcited by 3
  2. 2025StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Spacecited by 2
  3. 2026ID-Sim: An Identity-Focused Similarity Metriccited by 1