DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers
The remarkable scalability of Transformers has expanded their application to 3D computer vision, where camera-aware positional encoding is crucial for providing spatial cues in multi-view geometry. Recent advancements have established the practice of using camera parameters -- such as extrinsics or projection matrices -- as relative positional encoding into the query, key, and value vectors of the attention mechanism. However, when scaling up the training recipe of novel view synthesis (NVS) models with the camera-based positional encoding, we observe a significant issue: model performance stagnates in the late stages of training. In this paper, we investigate the cause of the performance...
| Authors | Shun Kenney, Teppei Suzuki |
|---|---|
| Primary category | cs.CV |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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