Local Implicit Grid Representations for 3D Scenes
Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas A. Funkhouser
Abstract
Training parts from ShapeNet. (b) t-SNE plot of part embeddings. (c) Reconstructing entire scenes with Local Implicit Grids Figure 1: We learn an embedding of parts from objects in ShapeNet [3] using a part autoencoder with an implicit decoder. We show that this representation of parts is generalizable across object categories, and easily scalable to large scenes. By localizing implicit functions in a grid, we are able to reconstruct entire scenes from points via optimization of the latent grid.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 1a379633-7d2b-4aa5-973a-40c1e129ed5fCited by top-tier papers239
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 4,089 citations
- Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional DomainsMatthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil et al.NeurIPS 2020 · 4,036 citations
- Implicit Neural Representations with Periodic Activation FunctionsVincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell et al.NeurIPS 2020 · 4,008 citations
- GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisKatja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas GeigerNeurIPS 2020 · 1,001 citations
- Efficient Geometry-aware 3D Generative Adversarial NetworksEric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano et al.CVPR 2022 · 984 citations
Builds on4
- PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human DigitizationShunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima et al.ICCV 2019 · 1,411 citations
- PointFlow: 3D Point Cloud Generation With Continuous Normalizing FlowsGuandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu et al.ICCV 2019 · 794 citations
- Learning Shape Templates With Structured Implicit FunctionsKyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna et al.ICCV 2019 · 427 citations
- DDSL: Deep Differentiable Simplex Layer for Learning Geometric SignalsChiyu Max Jiang, Dana Lynn Ona Lansigan, Philip Marcus, Matthias NießnerICCV 2019 · 12 citations
Related papers
- EditVAE: Unsupervised Parts-Aware Controllable 3D Point Cloud Shape GenerationShidi Li, Miaomiao Liu, Christian WalderAAAI 2022 · 35 citations
- DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentationZhiqin Chen, Qimin Chen, Hang Zhou, Hao ZhangSIGGRAPH 2024 · 9 citations
- Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D ScansAleksei Bokhovkin, Angela DaiCVPR 2023
- CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive AssemblyFenggen Yu, Zhiqin Chen, Manyi Li, Aditya Sanghi et al.CVPR 2022 · 53 citations
- UCLID-Net: Single View Reconstruction in Object SpaceBenoît Guillard, Edoardo Remelli, Pascal FuaNeurIPS 2020 · 8 citations
