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AIMS: Anchor-Integrated Multi-View Synthesis for Scalable Novel View Rendering
Computer Vision and Pattern Recognition · Submitted 6 Oct 2026
abstract compiled · 2026-10-07 · no language-model review
The authors' main claim Evaluations on RealEstate10K and ScanNet demonstrate a favorable quality--efficiency trade-off against transformer-based and Gaussian-based baselines.
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The abstract, sentence by sentence
Feed-forward novel view synthesis methods achieve strong generalization from posed multi-view inputs, but scaling them to large input view sets remains challenging. Transformer-based approaches that jointly process all input-view tokens incur rapidly increasing computation and memory as the number of views grows, while simple view subsampling discards potentially useful observations. We introduce Anchor-Integrated Multi-View Synthesis (AIMS), a scalable framework that decouples the number of available observations from the number of views processed by the global synthesis model. AIMS selects a fixed set of spatially distributed anchor views using farthest point sampling, groups nearby observations around each anchor, and uses a lightweight learnable integrator to fuse their information into enriched anchor representations. This allows additional observations to contribute to synthesis while keeping the downstream global view budget fixed. Evaluations on RealEstate10K and ScanNet demonstrate a favorable quality--efficiency trade-off against transformer-based and Gaussian-based baselines. AIMS achieves 29.41 dB and 17.73 dB PSNR on the two datasets, respectively, with rendering averaging 7.24 ms per view.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Feed-forward novel view synthesis methods achieve strong generalization from posed multi-view inputs, but scaling them to large input view sets remains challenging. | background 0.88 | 0.05 | 0.03 | asserted 1.00 |
| s1 | Transformer-based approaches that jointly process all input-view tokens incur rapidly increasing computation and memory as the number of views grows, while simple view subsampling discards potentially useful observations. | background 0.88 | 0.10 | 0.03 | asserted 1.00 |
| s2 | We introduce Anchor-Integrated Multi-View Synthesis (AIMS), a scalable framework that decouples the number of available observations from the number of views processed by the global synthesis model. | contribution 0.99 | 0.89 | 0.27 | asserted 1.00 |
| s3 | AIMS selects a fixed set of spatially distributed anchor views using farthest point sampling, groups nearby observations around each anchor, and uses a lightweight learnable integrator to fuse their information into enriched anchor representations. | method 0.99 | 0.93 | 0.08 | asserted 0.99 |
| s4 | This allows additional observations to contribute to synthesis while keeping the downstream global view budget fixed. | interpretation 0.43 | 0.67 | 0.13 | asserted 0.95 |
| s5 | Evaluations on RealEstate10K and ScanNet demonstrate a favorable quality--efficiency trade-off against transformer-based and Gaussian-based baselines. | result 0.90 | 0.94 | 0.56 | asserted 1.00 |
| s6 | AIMS achieves 29.41 dB and 17.73 dB PSNR on the two datasets, respectively, with rendering averaging 7.24 ms per view. | result 1.00 | 0.96 | 0.17 | asserted 1.00 |
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The paper
Original abstract
Feed-forward novel view synthesis methods achieve strong generalization from posed multi-view inputs, but scaling them to large input view sets remains challenging. Transformer-based approaches that jointly process all input-view tokens incur rapidly increasing computation and memory as the number of views grows, while simple view subsampling discards potentially useful observations. We introduce Anchor-Integrated Multi-View Synthesis (AIMS), a scalable framework that decouples the number of available observations from the number of views processed by the global synthesis model. AIMS selects a fixed set of spatially distributed anchor views using farthest point sampling, groups nearby observations around each anchor, and uses a lightweight learnable integrator to fuse their information into enriched anchor representations. This allows additional observations to contribute to synthesis while keeping the downstream global view budget fixed. Evaluations on RealEstate10K and ScanNet demonstrate a favorable quality--efficiency trade-off against transformer-based and Gaussian-based baselines. AIMS achieves 29.41 dB and 17.73 dB PSNR on the two datasets, respectively, with rendering averaging 7.24 ms per view.
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