Papers › Is Attention All That NeRF Needs?

Is Attention All That NeRF Needs?

27 Jul 2022arXiv:2207.13298archive 2025-07-28

Mukund Varma T, Peihao Wang, Xuxi Chen, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang

We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that reconstructs Neural Radiance Fields (NeRFs) and learns to renders novel views on the fly from source views. While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural representation and rendering that generalizes across scenes using transformers at two stages. (1) The view transformer leverages multi-view geometry as an inductive bias for attention-based scene representation, and predicts coordinate-aligned features by aggregating information from epipolar lines on the neighboring views. (2) The ray transformer renders novel views using attention to decode the features from the view transformer along the sampled points during ray marching. Our experiments demonstrate that when optimized on a single scene, GNT can successfully reconstruct NeRF without an explicit rendering formula due to the learned ray renderer. When trained on multiple scenes, GNT consistently achieves state-of-the-art performance when transferring to unseen scenes and outperform all other methods by ~10% on average. Our analysis of the learned attention maps to infer depth and occlusion indicate that attention enables learning a physically-grounded rendering. Our results show the promise of transformers as a universal modeling tool for graphics. Please refer to our project page for video results: https://vita-group.github.io/GNT/.

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Attention vita-group/gnt/gnt/transformer_network.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 0c8d8666ea836326 · report
Embedder vita-group/gnt/gnt/transformer_network.py community (archive-listed) ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 274e943335de6884 · report
FeedForward vita-group/gnt/gnt/transformer_network.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 0793903038531eaf · report
Transformer vita-group/gnt/gnt/transformer_network.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 2efce5965df73946 · report
Transformer2D vita-group/gnt/gnt/transformer_network.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · 92fa9a2a2a001168 · report
Attention2D vita-group/gnt/gnt/transformer_network.py community (archive-listed) unverified MIT (permissive) · 6d4a16676ba59f68 · report
GNT vita-group/gnt/gnt/transformer_network.py community (archive-listed) unverified MIT (permissive) · 1ec0574f22121353 · report

Tasks

AllGeneralizable Novel View SynthesisInductive BiasNeRFSSIM

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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