ORCA: Occlusion-Aware Refinement and Completion for Novel View Synthesis

2026-09-15

AI Summary

ORCA针对单图像新视图合成中的遮挡问题，通过引入3D结构和局部优化方法来修复小范围缺失区域，减少对生成性修补的依赖，从而提高新视图质量。

Abstract

Novel-view synthesis from a single image is a fundamentally ambiguous problem. As the camera moves away from the input viewpoint, previously hidden regions become visible, exposing missing geometry and holes in the reconstructed scene. Existing methods often rely on generative models to complete such regions. However, many of these artifacts are small gaps near depth boundaries and do not require generating new scene content. In order to eliminate expensive process of generating image we introduce ORCA, an occlusion-aware method for reconstructing and completing explorable 3D scenes from a single image. ORCA first introduces 3D structure into a Gaussian-anchor representation using monocular depth while preserving the original camera-ray correspondence. During scene exploration, missing regions are handled based on their size and structure. Small disocclusions are repaired using RGB-D information already available in the reconstruction, while generative inpainting is reserved for larger regions that cannot be reliably recovered from the scene. New Gaussian anchors are added and optimized locally without modifying the existing representation. By reducing unnecessary reliance on generative inpainting, ORCA limits generation-induced hallucinations and better preserves the content and structure of the original scene. On DIV2K, ORCA improves novel-view quality over VistaDream across all reported metrics, increasing MUSIQ from 61.60 to 68.71 and CLIP-IQA from 0.474 to 0.574. These results show that many novel-view artifacts can be repaired effectively by reusing information already present in the reconstructed scene.

Problem

• novel view synthesis
• occlusion
• disocclusions
• generative inpainting
• 3D reconstruction

Innovation

• occlusion-aware
• Gaussian-anchor representation
• monocular depth
• generative inpainting
• scene exploration