DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers

2026-06-30

AI Summary

This work addresses the limited scalability of multi-view Transformers due to performance saturation during training when using camera pose–based positional encoding. The authors identify that coupling rotational and translational components of camera poses within value vectors introduces ambiguity in view representation, hindering model scalability. To resolve this, they propose Decoupled Pose Positional Encoding (DPPE), the first method to explicitly separate rotation and translation in pose encoding while integrating relative positional information. DPPE significantly enhances training stability and generalization, achieving superior novel view synthesis under large-scale settings and demonstrating robustness in extrapolation scenarios—such as increased numbers of input views or changes in scene scale—where prior methods typically degrade.

Abstract

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 bottleneck when scaling up and demonstrate that storing rotation and translation given by the positional encoding in the same dimensions of the value vector causes indeterminacy in their independent identification, hindering training scalability. To address this, we propose Decoupled Pose Positional Encoding (DPPE), a novel camera-based positional encoding that explicitly decouples rotation and translation. Extensive evaluations on NVS tasks demonstrate that DPPE enables stable long-term training even in scaled-up training setup. Furthermore, it exhibits superior generalization performance in extrapolation settings, such as handling an increased number of viewpoints and zoom-in scenarios.

Problem

• camera-based positional encoding
• training scalability
• performance bottleneck
• multi-view transformers
• novel view synthesis

Innovation

• Decoupled Pose Positional Encoding
• camera-based positional encoding
• multi-view transformers
• novel view synthesis
• training scalability