2023-04-19 更新
NeRF applied to satellite imagery for surface reconstruction
Authors:Federico Semeraro, Yi Zhang, Wenying Wu, Patrick Carroll
We present Surf-NeRF, a modified implementation of the recently introduced Shadow Neural Radiance Field (S-NeRF) model. This method is able to synthesize novel views from a sparse set of satellite images of a scene, while accounting for the variation in lighting present in the pictures. The trained model can also be used to accurately estimate the surface elevation of the scene, which is often a desirable quantity for satellite observation applications. S-NeRF improves on the standard Neural Radiance Field (NeRF) method by considering the radiance as a function of the albedo and the irradiance. Both these quantities are output by fully connected neural network branches of the model, and the latter is considered as a function of the direct light from the sun and the diffuse color from the sky. The implementations were run on a dataset of satellite images, augmented using a zoom-and-crop technique. A hyperparameter study for NeRF was carried out, leading to intriguing observations on the model’s convergence. Finally, both NeRF and S-NeRF were run until 100k epochs in order to fully fit the data and produce their best possible predictions. The code related to this article can be found at https://github.com/fsemerar/surfnerf.
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CAT-NeRF: Constancy-Aware Tx$^2$Former for Dynamic Body Modeling
Authors:Haidong Zhu, Zhaoheng Zheng, Wanrong Zheng, Ram Nevatia
This paper addresses the problem of human rendering in the video with temporal appearance constancy. Reconstructing dynamic body shapes with volumetric neural rendering methods, such as NeRF, requires finding the correspondence of the points in the canonical and observation space, which demands understanding human body shape and motion. Some methods use rigid transformation, such as SE(3), which cannot precisely model each frame’s unique motion and muscle movements. Others generate the transformation for each frame with a trainable network, such as neural blend weight field or translation vector field, which does not consider the appearance constancy of general body shape. In this paper, we propose CAT-NeRF for self-awareness of appearance constancy with Tx$^2$Former, a novel way to combine two Transformer layers, to separate appearance constancy and uniqueness. Appearance constancy models the general shape across the video, and uniqueness models the unique patterns for each frame. We further introduce a novel Covariance Loss to limit the correlation between each pair of appearance uniquenesses to ensure the frame-unique pattern is maximally captured in appearance uniqueness. We assess our method on H36M and ZJU-MoCap and show state-of-the-art performance.
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NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
Authors:Jianlin Liu, Qiang Nie, Yong Liu, Chengjie Wang
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field(NeRF) is chosen as the 3D scene representation in our pipeline, which supports continuous 3D descriptors generation and neural rendering. By unifying the feature matching and the scene coordinate regression to the same framework, our model learns both generalizable knowledge and scene prior respectively during two training stages. Furthermore, to improve the localization robustness when domain gap exists between training and testing phases, we propose an appearance adaptation layer to explicitly align styles between the 3D model and the query image. Experiments show that our method achieves higher localization accuracy than other learning-based approaches on multiple benchmarks. Code is available at \url{https://github.com/JenningsL/nerf-loc}.
PDF accepted by ICRA 2023
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MoDA: Modeling Deformable 3D Objects from Casual Videos
Authors:Chaoyue Song, Tianyi Chen, Yiwen Chen, Jiacheng Wei, Chuan Sheng Foo, Fayao Liu, Guosheng Lin
In this paper, we focus on the challenges of modeling deformable 3D objects from casual videos. With the popularity of neural radiance fields (NeRF), many works extend it to dynamic scenes with a canonical NeRF and a deformation model that achieves 3D point transformation between the observation space and the canonical space. Recent works rely on linear blend skinning (LBS) to achieve the canonical-observation transformation. However, the linearly weighted combination of rigid transformation matrices is not guaranteed to be rigid. As a matter of fact, unexpected scale and shear factors often appear. In practice, using LBS as the deformation model can always lead to skin-collapsing artifacts for bending or twisting motions. To solve this problem, we propose neural dual quaternion blend skinning (NeuDBS) to achieve 3D point deformation, which can perform rigid transformation without skin-collapsing artifacts. Besides, we introduce a texture filtering approach for texture rendering that effectively minimizes the impact of noisy colors outside target deformable objects. Extensive experiments on real and synthetic datasets show that our approach can reconstruct 3D models for humans and animals with better qualitative and quantitative performance than state-of-the-art methods.
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NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination
Authors:Yiyu Zhuang, Qi Zhang, Xuan Wang, Hao Zhu, Ying Feng, Xiaoyu Li, Ying Shan, Xun Cao
Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.
PDF Project page: https://yiyuzhuang.github.io/NeAI/
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SurfelNeRF: Neural Surfel Radiance Fields for Online Photorealistic Reconstruction of Indoor Scenes
Authors:Yiming Gao, Yan-Pei Cao, Ying Shan
Online reconstructing and rendering of large-scale indoor scenes is a long-standing challenge. SLAM-based methods can reconstruct 3D scene geometry progressively in real time but can not render photorealistic results. While NeRF-based methods produce promising novel view synthesis results, their long offline optimization time and lack of geometric constraints pose challenges to efficiently handling online input. Inspired by the complementary advantages of classical 3D reconstruction and NeRF, we thus investigate marrying explicit geometric representation with NeRF rendering to achieve efficient online reconstruction and high-quality rendering. We introduce SurfelNeRF, a variant of neural radiance field which employs a flexible and scalable neural surfel representation to store geometric attributes and extracted appearance features from input images. We further extend the conventional surfel-based fusion scheme to progressively integrate incoming input frames into the reconstructed global neural scene representation. In addition, we propose a highly-efficient differentiable rasterization scheme for rendering neural surfel radiance fields, which helps SurfelNeRF achieve $10\times$ speedups in training and inference time, respectively. Experimental results show that our method achieves the state-of-the-art 23.82 PSNR and 29.58 PSNR on ScanNet in feedforward inference and per-scene optimization settings, respectively.
PDF To appear in CVPR 2023
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