mlsanityData to 7 Oct 2026

GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding

arXiv 2403.03608 ↗First posted 6 Mar 2024cs.CV cs.AI cs.LGcited by 15

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Abstract (arXiv)

Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Generalizable Semantic Neural Radiance Field (GSNeRF), which uniquely takes image semantics into the synthesis process so that both novel view images and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic Geo-Reasoning and Depth-Guided Visual rendering. The former is able to observe multi-view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information, the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 41 of its 46 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

41 of 46 references.

References · 41

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29 more
  1. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
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  5. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 732
  6. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 672
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  19. 2022Is Attention All That NeRF Needs?cited by 113
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Citing papers per year: 2024: 3; 2025: 8; 2026: 4.

15 papers.

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