Knowledge NeRF: Few-shot Novel View Synthesis for Dynamic Articulated Objects
- URL: http://arxiv.org/abs/2404.00674v2
- Date: Sun, 7 Apr 2024 01:56:15 GMT
- Title: Knowledge NeRF: Few-shot Novel View Synthesis for Dynamic Articulated Objects
- Authors: Wenxiao Cai, Xinyue Lei, Xinyu He, Junming Leo Chen, Yangang Wang,
- Abstract summary: We present Knowledge NeRF to synthesize novel views for dynamic scenes.
We pretrain a NeRF model for an articulated object.When articulated objects moves, Knowledge NeRF learns to generate novel views at the new state.
- Score: 8.981452149411714
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present Knowledge NeRF to synthesize novel views for dynamic scenes. Reconstructing dynamic 3D scenes from few sparse views and rendering them from arbitrary perspectives is a challenging problem with applications in various domains. Previous dynamic NeRF methods learn the deformation of articulated objects from monocular videos. However, qualities of their reconstructed scenes are limited. To clearly reconstruct dynamic scenes, we propose a new framework by considering two frames at a time.We pretrain a NeRF model for an articulated object.When articulated objects moves, Knowledge NeRF learns to generate novel views at the new state by incorporating past knowledge in the pretrained NeRF model with minimal observations in the present state. We propose a projection module to adapt NeRF for dynamic scenes, learning the correspondence between pretrained knowledge base and current states. Experimental results demonstrate the effectiveness of our method in reconstructing dynamic 3D scenes with 5 input images in one state. Knowledge NeRF is a new pipeline and promising solution for novel view synthesis in dynamic articulated objects. The data and implementation are publicly available at https://github.com/RussRobin/Knowledge_NeRF.
Related papers
- DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving scenes.
Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs.
arXiv Detail & Related papers (2024-06-17T21:15:13Z) - CTNeRF: Cross-Time Transformer for Dynamic Neural Radiance Field from Monocular Video [25.551944406980297]
We propose a novel approach to generate high-quality novel views from monocular videos of complex and dynamic scenes.
We introduce a module that operates in both the time and frequency domains to aggregate the features of object motion.
Our experiments demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets.
arXiv Detail & Related papers (2024-01-10T00:40:05Z) - Template-free Articulated Neural Point Clouds for Reposable View
Synthesis [11.535440791891217]
We present a novel method to jointly learn a Dynamic NeRF and an associated skeletal model from even sparse multi-view video.
Our forward-warping approach achieves state-of-the-art visual fidelity when synthesizing novel views and poses.
arXiv Detail & Related papers (2023-05-30T14:28:08Z) - OD-NeRF: Efficient Training of On-the-Fly Dynamic Neural Radiance Fields [63.04781030984006]
Dynamic neural radiance fields (dynamic NeRFs) have demonstrated impressive results in novel view synthesis on 3D dynamic scenes.
We propose OD-NeRF to efficiently train and render dynamic NeRFs on-the-fly which instead is capable of streaming the dynamic scene.
Our algorithm can achieve an interactive speed of 6FPS training and rendering on synthetic dynamic scenes on-the-fly, and a significant speed-up compared to the state-of-the-art on real-world dynamic scenes.
arXiv Detail & Related papers (2023-05-24T07:36:47Z) - StegaNeRF: Embedding Invisible Information within Neural Radiance Fields [61.653702733061785]
We present StegaNeRF, a method for steganographic information embedding in NeRF renderings.
We design an optimization framework allowing accurate hidden information extractions from images rendered by NeRF.
StegaNeRF signifies an initial exploration into the novel problem of instilling customizable, imperceptible, and recoverable information to NeRF renderings.
arXiv Detail & Related papers (2022-12-03T12:14:19Z) - ActiveNeRF: Learning where to See with Uncertainty Estimation [36.209200774203005]
Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images.
We present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget.
arXiv Detail & Related papers (2022-09-18T12:09:15Z) - Learning Multi-Object Dynamics with Compositional Neural Radiance Fields [63.424469458529906]
We present a method to learn compositional predictive models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural networks.
NeRFs have become a popular choice for representing scenes due to their strong 3D prior.
For planning, we utilize RRTs in the learned latent space, where we can exploit our model and the implicit object encoder to make sampling the latent space informative and more efficient.
arXiv Detail & Related papers (2022-02-24T01:31:29Z) - BARF: Bundle-Adjusting Neural Radiance Fields [104.97810696435766]
We propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from imperfect camera poses.
BARF can effectively optimize the neural scene representations and resolve large camera pose misalignment at the same time.
This enables view synthesis and localization of video sequences from unknown camera poses, opening up new avenues for visual localization systems.
arXiv Detail & Related papers (2021-04-13T17:59:51Z) - pixelNeRF: Neural Radiance Fields from One or Few Images [20.607712035278315]
pixelNeRF is a learning framework that predicts a continuous neural scene representation conditioned on one or few input images.
We conduct experiments on ShapeNet benchmarks for single image novel view synthesis tasks with held-out objects.
In all cases, pixelNeRF outperforms current state-of-the-art baselines for novel view synthesis and single image 3D reconstruction.
arXiv Detail & Related papers (2020-12-03T18:59:54Z) - D-NeRF: Neural Radiance Fields for Dynamic Scenes [72.75686949608624]
We introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain.
D-NeRF reconstructs images of objects under rigid and non-rigid motions from a camera moving around the scene.
We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions.
arXiv Detail & Related papers (2020-11-27T19:06:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.