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SAIL-Recon: Large SfM by Augmenting Scene Regression with Localization
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SAIL-Recon: Large SfM by Augmenting Scene Regression with Localization
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Scene regression methods, such as VGGT, solve the Structure-from-Motion (SfM) problem by directly regressing camera poses and 3D scene structures from input images. They demonstrate impressive performance in handling images under extreme viewpoint changes. However, these methods struggle to handle a large number of input images. To address this problem, we introduce SAIL-Recon, a feed-forward Transformer for large scale SfM, by augmenting the scene regression network with visual localization capabilities. Specifically, our method first computes a neural scene representation from a subset of anchor images. The regression network is then fine-tuned to reconstruct all input images conditioned on this neural scene representation. Comprehensive experiments show that our method not only scales efficiently to large-scale scenes, but also achieves state-of-the-art results on both camera pose estimation and novel view synthesis benchmarks, including TUM-RGBD, CO3Dv2, and Tanks & Temples. We will publish our model and code. Code and models are publicly available at: https://hkust-sail.github.io/ sail-recon/.
Forward citations
Cited by 9 Pith papers
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FrameVGGT: Coherence-Preserving Memory for Bounded Streaming Geometry
FrameVGGT replaces token-level KV retention with frame-level segments and prototypes to bound memory while preserving geometric coherence in streaming VGGT.
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Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes
Argus is a feed-forward network for metric panoramic 3D reconstruction, trained on the new Realsee3D dataset of 10K indoor scenes and using a learned covisibility module plus decomposed mapping supervision to achieve ...
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Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes
Argus plus Realsee3D deliver state-of-the-art metric camera pose, depth, and point-cloud reconstruction from unordered indoor panoramas via learned covisibility anchoring and geometric factorization.
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Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes
Argus introduces a covisibility module and decomposed pixel-to-world mapping to deliver SOTA metric performance on camera pose, depth, and point cloud tasks using the Realsee3D panoramic dataset.
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EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation
EPS3D is an end-to-end architecture for 3D panoptic segmentation from multi-view images that uses distillation and semantic-instance mutual enhancement to achieve higher benchmark performance and speed than prior methods.
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Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction
Scal3R achieves better accuracy and consistency in large-scale 3D scene reconstruction by maintaining a compressed global context through test-time adaptation of lightweight neural networks on long video sequences.
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Depth Anything 3: Recovering the Visual Space from Any Views
DA3 recovers consistent visual geometry from arbitrary views via a vanilla DINO transformer and depth-ray target, setting new SOTA on a visual geometry benchmark while outperforming DA2 on monocular depth.
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Global Structure-from-Motion Meets Feedforward Reconstruction
A new SfM pipeline combining classical and feedforward methods reports state-of-the-art results across multiple datasets and is released as open source.
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FrameVGGT: Coherence-Preserving Memory for Bounded Streaming Geometry
FrameVGGT maintains stable long-horizon 3D reconstruction, depth, and pose under fixed memory by organizing history as complementary frame-wise KV prototypes plus sparse anchors.
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