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Metropolis Light Transport (MLT) is a global illumination algorithm that is well-known for rendering challenging scenes with intricate light paths. However, MLT methods tend to produce unpredictable correlation artifacts in images, which can introduce visual inconsistencies for animation rendering. This drawback also makes it challenging to denoise MLT renderings while maintaining temporal stability. We tackle this issue with modern learning-based methods and build a sequence denoiser combining the recurrent connections with the cutting-edge vision transformer architecture. We demonstrate that our sophisticated denoiser can consistently improve the quality and temporal stability of MLT renderings with difficult light paths. Our method is efficient and scalable for complex scene renderings that require high sample counts.
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This content will become publicly available on June 9, 2026
Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features
Visual aliasing, or doppelgangers, poses severe challenges to 3D reconstruction. We propose Doppelganger++, an enhanced pairwise image classifier that excels in visual disambiguation across diverse and challenging scenes. We seamlessly integrate Doppelganger++ into SfM, successfully disambiguating each scene. (Middle) Compared to prior work (which we refer to as DG-OG), Doppelgangers++ is more robust for everyday scenes, showing improved accuracy and robustness. We show pairs that DG-OG classifies incorrectly and ours gets correct. Our new VisymScenes dataset, featuring complex daily scenes, is particularly challenging for COLMAP and DG-OG, but our method can achieve correct and complete reconstructions.
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- Award ID(s):
- 2212084
- PAR ID:
- 10601177
- Publisher / Repository:
- Computer Vision and Pattern Recognition (CVPR)
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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