Summary of Divide and Conquer: Rethinking the Training Paradigm Of Neural Radiance Fields, by Rongkai Ma et al.
Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields
by Rongkai Ma, Leo Lebrat, Rodrigo Santa Cruz, Gil Avraham, Yan Zuo, Clinton Fookes, Olivier Salvado
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reexamines the standard training paradigm for neural radiance fields (NeRFs) and proposes an alternative approach to improve rendering quality. NeRFs have shown promise in synthesizing high-fidelity views of 3D scenes, but traditional training methods assume equal importance for each image. This assumption hinders rendering specific views with intricate geometries, resulting in suboptimal performance. To address this limitation, the authors divide input views into groups based on visual similarities and train individual models on each group. These specialized models are then aggregated via a teacher-student distillation paradigm to enable spatial efficiency for online rendering. The proposed DaC training pipeline is evaluated on two publicly available datasets (NeRF synthetic and Tanks&Temples), demonstrating enhanced rendering quality compared to state-of-the-art baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can create very realistic pictures of 3D objects from different angles. Right now, these “neural radiance fields” are good at creating general views, but they struggle with complex shapes or specific viewpoints. The authors suggest a new way to train these models by grouping similar images together and training separate models for each group. These specialized models can then be combined to create better results. They tested this approach on two big datasets and found that it works better than the current method. |
Keywords
» Artificial intelligence » Distillation
