EcoSplat: Efficiency-controllable Feed-forward 3D Gaussian Splatting from Multi-view Images
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912780399935488 |
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| author | Park, Jongmin Bui, Minh-Quan Viet Bello, Juan Luis Gonzalez Moon, Jaeho Oh, Jihyong Kim, Munchurl |
| author_facet | Park, Jongmin Bui, Minh-Quan Viet Bello, Juan Luis Gonzalez Moon, Jaeho Oh, Jihyong Kim, Munchurl |
| contents | Feed-forward 3D Gaussian Splatting (3DGS) enables efficient one-pass scene reconstruction, providing 3D representations for novel view synthesis without per-scene optimization. However, existing methods typically predict pixel-aligned primitives per-view, producing an excessive number of primitives in dense-view settings and offering no explicit control over the number of predicted Gaussians. To address this, we propose EcoSplat, the first efficiency-controllable feed-forward 3DGS framework that adaptively predicts the 3D representation for any given target primitive count at inference time. EcoSplat adopts a two-stage optimization process. The first stage is Pixel-aligned Gaussian Training (PGT) where our model learns initial primitive prediction. The second stage is Importance-aware Gaussian Finetuning (IGF) stage where our model learns rank primitives and adaptively adjust their parameters based on the target primitive count. Extensive experiments across multiple dense-view settings show that EcoSplat is robust and outperforms state-of-the-art methods under strict primitive-count constraints, making it well-suited for flexible downstream rendering tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18692 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EcoSplat: Efficiency-controllable Feed-forward 3D Gaussian Splatting from Multi-view Images Park, Jongmin Bui, Minh-Quan Viet Bello, Juan Luis Gonzalez Moon, Jaeho Oh, Jihyong Kim, Munchurl Computer Vision and Pattern Recognition Feed-forward 3D Gaussian Splatting (3DGS) enables efficient one-pass scene reconstruction, providing 3D representations for novel view synthesis without per-scene optimization. However, existing methods typically predict pixel-aligned primitives per-view, producing an excessive number of primitives in dense-view settings and offering no explicit control over the number of predicted Gaussians. To address this, we propose EcoSplat, the first efficiency-controllable feed-forward 3DGS framework that adaptively predicts the 3D representation for any given target primitive count at inference time. EcoSplat adopts a two-stage optimization process. The first stage is Pixel-aligned Gaussian Training (PGT) where our model learns initial primitive prediction. The second stage is Importance-aware Gaussian Finetuning (IGF) stage where our model learns rank primitives and adaptively adjust their parameters based on the target primitive count. Extensive experiments across multiple dense-view settings show that EcoSplat is robust and outperforms state-of-the-art methods under strict primitive-count constraints, making it well-suited for flexible downstream rendering tasks. |
| title | EcoSplat: Efficiency-controllable Feed-forward 3D Gaussian Splatting from Multi-view Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.18692 |