Cameras as Relative Positional Encoding
Not on the map. Why?
Cited by 27 in World models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Transformers are increasingly prevalent for multi-view computer vision tasks, where geometric relationships between viewpoints are critical for 3D perception. To leverage these relationships, multi-view transformers must use camera geometry to ground visual tokens in 3D space. In this work, we compare techniques for conditioning transformers on cameras: token-level raymap encodings, attention-level relative pose encodings, and a new relative encoding we propose -- Projective Positional Encoding (PRoPE) -- that captures complete camera frustums, both intrinsics and extrinsics, as a relative positional encoding. Our experiments begin by showing how relative camera conditioning improves performance in feedforward novel view synthesis, with further gains from PRoPE. This holds across settings: scenes with both shared and varying intrinsics, when combining token- and attention-level conditioning, and for generalization to inputs with out-of-distribution sequence lengths and camera intrinsics. We then verify that these benefits persist for different tasks, stereo depth estimation and discriminative spatial cognition, as well as larger model sizes.
Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). Builds on 58 of its 71 references that are in the map. First posted on arXiv 14 Jul 2025.
Builds on
58 of 71 references.
References · 58
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- 2024CAT4D: Create Anything in 4D with Multi-View Video Diffusion Modelscited by 160
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- 2024MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Viewscited by 111
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- 2024FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descentcited by 61
- 20233D Concept Learning and Reasoning from Multi-View Imagescited by 60
50 most cited of 58 All 58 (JSON)
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