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A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond. [PDF]
Plant PhenomicsPlant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment, making it crucial for advancing precision agriculture and crop improvement.
Li J +8 more
europepmc +3 more sources
SLAM Meets NeRF: A Survey of Implicit SLAM Methods
World Electric Vehicle JournalIn recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented.
Zonghai Chen, Jikai Wang, Yunqi Cheng
exaly +3 more sources
Geo-spatial Information ScienceTrue Digital Orthophoto Maps (TDOMs) have high geometric accuracy and rich image characteristics, making them essential geographic data for national economic and social development.
Shihan Chen +5 more
doaj +2 more sources
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields [PDF]
IEEE International Conference on Computer Vision, 2021 The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different ...
J. Barron +5 more
semanticscholar +1 more source
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields [PDF]
Computer Vision and Pattern Recognition, 2021 Though neural radiance fields (NeRF) have demon-strated impressive view synthesis results on objects and small bounded regions of space, they struggle on “un-bounded” scenes, where the camera may point in any di-rection and content may exist at any ...
J. Barron +4 more
semanticscholar +1 more source
Depth-supervised NeRF: Fewer Views and Faster Training for Free [PDF]
Computer Vision and Pattern Recognition, 2021 A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a ...
Kangle Deng +3 more
semanticscholar +1 more source
SPARSESAT-NERF: DENSE DEPTH SUPERVISED NEURAL RADIANCE FIELDS FOR SPARSE SATELLITE IMAGES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Digital surface model generation using traditional multi-view stereo matching (MVS) performs poorly over non-Lambertian surfaces, with asynchronous acquisitions, or at discontinuities. Neural radiance fields (NeRF) offer a new paradigm for reconstructing
L. Zhang, L. Zhang, E. Rupnik
doaj +1 more source
Block-NeRF: Scalable Large Scene Neural View Synthesis [PDF]
Computer Vision and Pattern Recognition, 2022 We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to de-compose the scene into ...
Matthew Tancik +7 more
semanticscholar +1 more source
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures [PDF]
Computer Vision and Pattern Recognition, 2022 Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score distillation, one can successfully text-guide a NeRF model to generate a 3D ...
G. Metzer +4 more
semanticscholar +1 more source
Point-NeRF: Point-based Neural Radiance Fields [PDF]
Computer Vision and Pattern Recognition, 2022 Volumetric neural rendering methods like NeRF [34] generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time.
Qiangeng Xu +6 more
semanticscholar +1 more source