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LaRa: Efficient Large-Baseline Radiance Fields

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arxiv 2407.04699 v2 pith:5425HFB6 submitted 2024-07-05 cs.CV cs.AI

LaRa: Efficient Large-Baseline Radiance Fields

Anpei Chen , Haofei Xu , Stefano Esposito , Siyu Tang , Andreas Geiger This is my paper
classification cs.CV cs.AI
keywords reconstructionradianceattentionefficientfeed-forwardfieldsgloballara
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Radiance field methods have achieved photorealistic novel view synthesis and geometry reconstruction. But they are mostly applied in per-scene optimization or small-baseline settings. While several recent works investigate feed-forward reconstruction with large baselines by utilizing transformers, they all operate with a standard global attention mechanism and hence ignore the local nature of 3D reconstruction. We propose a method that unifies local and global reasoning in transformer layers, resulting in improved quality and faster convergence. Our model represents scenes as Gaussian Volumes and combines this with an image encoder and Group Attention Layers for efficient feed-forward reconstruction. Experimental results demonstrate that our model, trained for two days on four GPUs, demonstrates high fidelity in reconstructing 360 deg radiance fields, and robustness to zero-shot and out-of-domain testing. Our project Page: https://apchenstu.github.io/LaRa/.

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