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Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields

Exact-NeRF: An Exploration of a Precise Volumetric Parameter...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Isaac-Medina, Brian K. S. Willcocks, Chris G. Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (纸本)9798350301298

Neural Radiance Fields (NeRF) have attracted significant attention due to their ability to synthesize novel scene views with great accuracy. However, inherent to their underlying formulation, the sampling of points along a ray with zero width may result in ambiguous representations that lead to further rendering artifacts such as aliasing in the final scene. To address this issue, the recent variant mip-NeRF proposes an Integrated Positional Encoding (IPE) based on a conical view frustum. Although this is expressed with an integral formulation, mip-NeRF instead approximates this integral as the expected value of a multivariate Gaussian distribution. This approximation is reliable for short frustums but degrades with highly elongated regions, which arises when dealing with distant scene objects under a larger depth of field. In this paper, we explore the use of an exact approach for calculating the IPE by using a pyramid-based integral formulation instead of an approximated conical-based one. We denote this formulation as Exact-NeRF and contribute the first approach to offer a precise analytical solution to the IPE within the NeRF domain. Our exploratory work illustrates that such an exact formulation (Exact-NeRF) matches the accuracy of mip-NeRF and furthermore provides a natural extension to more challenging scenarios without further modification, such as in the case of unbounded scenes. Our contribution aims to both address the hitherto unexplored issues of frustum approximation in earlier NeRF work and additionally provide insight into the potential future consideration of analytical solutions in future NeRF extensions.

关键词： 3d from multi-view and sensors

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dyLiN: Making Light Field Networks dynamic

DyLiN: Making Light Field Networks Dynamic

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu, Heng Julin, Joel Milacski, Zoltan A. Niinuma, Koichiro Jeni, Laszlo A. Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Fujitsu Res Amer Sunnyvale CA USA

ISBN: (纸本)9798350301298

Light Field Networks, the re-formulations of radiance fields to oriented rays, are magnitudes faster than their coordinate network counterparts, and provide higher fidelity with respect to representing 3d structures from 2d observations. They would be well suited for generic scene representation and manipulation, but suffer from one problem: they are limited to holistic and static scenes. In this paper, we propose the dynamic Light Field Network (dyLiN) method that can handle non-rigid deformations, including topological changes. We learn a deformation field from input rays to canonical rays, and lift them into a higher dimensional space to handle discontinuities. We further introduce wCodyLiN, which augments dyLiN with controllable attribute inputs. We train both models via knowledge distillation from pretrained dynamic radiance fields. We evaluated dyLiN using both synthetic and real world datasets that include various non-rigid deformations. dyLiN qualitatively outperformed and quantitatively matched state-of-the-art methods in terms of visual fidelity, while being 25 - 71x computationally faster. We also tested CodyLiN on attribute annotated data and it surpassed its teacher model. Project page: https://***.

关键词： 3d from multi-view and sensors

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Grid-guided Neural Radiance Fields for Large Urban Scenes

Grid-guided Neural Radiance Fields for Large Urban Scenes

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu, Tinning Xiangli, Yuanbo Peng, Sida Pan, Xingang Zhao, Nanxuan Theobalt, Christian dai, Bo Lin, dahua Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Max Planck Inst Informat Saarbrucken Germany Zhejiang Univ Hangzhou Peoples R China Adobe Res San Jose CA USA

ISBN: (纸本)9798350301298

Purely MLP-based neural radiance fields (NeRF-based methods) often suffer from underfitting with blurred renderings on large-scale scenes due to limited model capacity. Recent approaches propose to geographically divide the scene and adopt multiple sub-NeRFs to model each region individually, leading to linear scale-up in training costs and the number of sub-NeRFs as the scene expands. An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene with increased grid resolutions. However, the feature grid tends to be less constrained and often reaches suboptimal solutions, producing noisy artifacts in renderings, especially in regions with complex geometry and texture. In this work, we present a new framework that realizes high-fidelity rendering on large urban scenes while being computationally efficient. We propose to use a compact multi-resolution ground feature plane representation to coarsely capture the scene, and complement it with positional encoding inputs through another NeRF branch for rendering in a joint learning fashion. We show that such an integration can utilize the advantages of two alternative solutions: a light-weighted NeRF is sufficient, under the guidance of the feature grid representation, to render photorealistic novel views with fine details;and the jointly optimized ground feature planes, can meanwhile gain further refinements, forming a more accurate and compact feature space and output much more natural rendering results.

关键词： 3d from multi-view and sensors

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TensoIR: Tensorial Inverse Rendering

TensoIR: Tensorial Inverse Rendering

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jin, Haian Liu, Isabella Xu, Peijia Zhang, Xiaoshuai Han, Songfang Bi, Sai Zhou, Xiaowei Xu, Zexiang Su, Hao Zhejiang Univ Hangzhou Peoples R China Univ Calif San Diego San Diego CA USA Kingstar Technol Inc San Diego CA USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350301298

We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus suffering from low capacity and high computation costs, we extend TensoRF, a state-of-the-art approach for radiance field modeling, to estimate scene geometry, surface reflectance, and environment illumination from multiview images captured under unknown lighting conditions. Our approach jointly achieves radiance field reconstruction and physically-based model estimation, leading to photo-realistic novel view synthesis and relighting results. Benefiting from the efficiency and extensibility of the TensoRF-based representation, our method can accurately model secondary shading effects (like shadows and indirect lighting) and generally support input images captured under single or multiple unknown lighting conditions. The low-rank tensor representation allows us to not only achieve fast and compact reconstruction but also better exploit shared information under an arbitrary number of capturing lighting conditions. We demonstrate the superiority of our method to baseline methods qualitatively and quantitatively on various challenging synthetic and real-world scenes.

关键词： 3d from multi-view and sensors

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dINER: depth-aware Image-based NEural Radiance fields

DINER: Depth-aware Image-based NEural Radiance fields

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Prinzler, Malte Hilliges, Otmar Thies, Justus Max Planck Inst Intelligent Syst Tubingen Germany Swiss Fed Inst Technol Zurich Switzerland Max Planck ETH Ctr Learning Syst Stuttgart Germany

ISBN: (纸本)9798350301298

We present depth-aware Image-based NEural Radiance fields (dINER). Given a sparse set of RGB input views, we predict depth and feature maps to guide the reconstruction of a volumetric scene representation that allows us to render 3d objects under novel views. Specifically, we propose novel techniques to incorporate depth information into feature fusion and efficient scene sampling. In comparison to the previous state of the art, dINER achieves higher synthesis quality and can process input views with greater disparity. This allows us to capture scenes more completely without changing capturing hardware requirements and ultimately enables larger viewpoint changes during novel view synthesis. We evaluate our method by synthesizing novel views, both for human heads and for general objects, and observe significantly improved qualitative results and increased perceptual metrics compared to the previous state of the art. The code is publicly available through the Project Webpage.

关键词： 3d from multi-view and sensors

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Towards Unbiased Volume Rendering of Neural Implicit Surfaces with Geometry Priors

Towards Unbiased Volume Rendering of Neural Implicit Surface...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Yongqiang Hu, Zhipeng Wu, Haoqian Zhao, Minda Li, Lincheng Zou, Zhengxia Fan, Changjie NetEase Fuxi Lab Hangzhou Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Learning surface by neural implicit rendering has been a promising way for multi-view reconstruction in recent years. Existing neural surface reconstruction methods, such as NeuS [24] and VolSdF [32], can produce reliable meshes from multi-view posed images. Although they build a bridge between volume rendering and Signed distance Function (SdF), the accuracy is still limited. In this paper, we argue that this limited accuracy is due to the bias of their volume rendering strategies, especially when the viewing direction is close to be tangent to the surface. We revise and provide an additional condition for the unbiased volume rendering. Following this analysis, we propose a new rendering method by scaling the SdF field with the angle between the viewing direction and the surface normal vector. Experiments on simulated data indicate that our rendering method reduces the bias of SdF-based volume rendering. Moreover, there still exists non-negligible bias when the learnable standard deviation of SdF is large at early stage, which means that it is hard to supervise the rendered depth with depth priors. Alternatively we supervise zero-level set with surface points obtained from a pre-trained multi-view Stereo network. We evaluate our method on the dTU dataset and show that it outperforms the state-of-the-arts neural implicit surface methods without mask supervision.

关键词： 3d from multi-view and sensors

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LP-dIF: Learning Local Pattern-specific deep Implicit Function for 3d Objects and Scenes

LP-DIF: Learning Local Pattern-specific Deep Implicit Functi...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Meng Liu, Yu-Shen Gao, Yue Shi, Kanle Fang, Yi Han, Zhizhong Tsinghua Univ Sch Software BNRist Beijing Peoples R China Kuaishou Technol Beijing Peoples R China New York Univ Abu Dhabi Ctr Artificial Intelligence & Robot Abu Dhabi U Arab Emirates Wayne State Univ Dept Comp Sci Detroit MI USA

ISBN: (纸本)9798350301298

deep Implicit Function (dIF) has gained much popularity as an efficient 3d shape representation. To capture geometry details, current mainstream methods divide 3d shapes into local regions and then learn each one with a local latent code via a decoder. Such local methods can capture more local details due to less diversity among local regions than global shapes. Although the diversity of local regions has been decreased compared to global approaches, the diversity in different local regions still poses a challenge in learning an implicit function when treating all regions equally using only a single decoder. What is worse, these local regions often exhibit imbalanced distributions, where certain regions have significantly fewer observations. This leads that fine geometry details could not be preserved well. To solve this problem, we propose a novel Local Pattern-specific Implicit Function, named LP-dIF, to represent a shape with clusters of local regions and multiple decoders, where each decoder only focuses on one cluster of local regions which share a certain pattern. Specifically, we first extract local codes for all regions, and then cluster them into multiple groups in the latent space, where similar regions sharing a common pattern fall into one group. After that, we train multiple decoders for mining local patterns of different groups, which simplifies the learning of fine geometric details by reducing the diversity of local regions seen by each decoder. To further alleviate the data-imbalance problem, we introduce a region re-weighting module to each pattern-specific decoder using a kernel density estimator, which dynamically re-weights the regions during learning. Our LP-dIF can restore more geometry details, and thus improve the quality of 3d reconstruction. Experiments demonstrate that our method can achieve the state-of-the-art performance over previous methods. Code is available at https://***/gtyxyz/lpdif.

关键词： 3d from multi-view and sensors

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diffRF: Rendering-Guided 3d Radiance Field diffusion

DiffRF: Rendering-Guided 3D Radiance Field Diffusion

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Mueller, Norman Siddiqui, Yawar Porzi, Lorenzo Bulo, Samuel Rota Kontschieder, Peter Niessner, Matthias Tech Univ Munich Munich Germany Meta Real Labs Zurich Zurich Switzerland

ISBN: (纸本)9798350301298

We introduce diffRF, a novel approach for 3d radiance field synthesis based on denoising diffusion probabilistic models. While existing diffusion-based methods operate on images, latent codes, or point cloud data, we are the first to directly generate volumetric radiance fields. To this end, we propose a 3d denoising model which directly operates on an explicit voxel grid representation. However, as radiance fields generated from a set of posed images can be ambiguous and contain artifacts, obtaining ground truth radiance field samples is non-trivial. We address this challenge by pairing the denoising formulation with a rendering loss, enabling our model to learn a deviated prior that favours good image quality instead of trying to replicate fitting errors like floating artifacts. In contrast to 2d-diffusion models, our model learns multi-view consistent priors, enabling free-view synthesis and accurate shape generation. Compared to 3d GANs, our diffusion-based approach naturally enables conditional generation such as masked completion or single-view 3d synthesis at inference time.

关键词： 3d from multi-view and sensors

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High-Res Facial Appearance Capture from Polarized Smartphone Images

High-Res Facial Appearance Capture from Polarized Smartphone...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Azinovic, dejan Maury, Olivier Hery, Christophe Niessner, Matthias Thies, Justus Tech Univ Munich Munich Germany Meta Real Labs Seattle WA USA Max Planck Inst Intelligent Syst Stuttgart Germany

ISBN: (纸本)9798350301298

We propose a novel method for high-quality facial texture reconstruction from RGB images using a novel capturing routine based on a single smartphone which we equip with an inexpensive polarization foil. Specifically, we turn the flashlight into a polarized light source and add a polarization filter on top of the camera. Leveraging this setup, we capture the face of a subject with cross-polarized and parallel-polarized light. For each subject, we record two short sequences in a dark environment under flash illumination with different light polarization using the modified smartphone. Based on these observations, we reconstruct an explicit surface mesh of the face using structure from motion. We then exploit the camera and light co-location within a differentiable renderer to optimize the facial textures using an analysis-by-synthesis approach. Our method optimizes for high-resolution normal textures, diffuse albedo, and specular albedo using a coarse-to-fine optimization scheme. We show that the optimized textures can be used in a standard rendering pipeline to synthesize high-quality photo-realistic 3d digital humans in novel environments.

关键词： 3d from multi-view and sensors

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viewpoint Equivariance for multi-view 3d Object detection

Viewpoint Equivariance for Multi-View 3D Object Detection

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, dian Li, Jie Guizilini, Vitor Ambrus, Rares Gaidon, Adrien Toyota Res Inst TRI Los Altos CA 94022 USA

ISBN: (纸本)9798350301298

3d object detection from visual sensors is a cornerstone capability of robotic systems. State-of-the-art methods focus on reasoning and decoding object bounding boxes from multi-view camera input. In this work we gain intuition from the integral role of multi-view consistency in 3d scene understanding and geometric learning. To this end, we introduce VEdet, a novel 3d object detection framework that exploits 3d multi-view geometry to improve localization through viewpoint awareness and equivariance. VEdet leverages a query-based transformer architecture and encodes the 3d scene by augmenting image features with positional encodings from their 3d perspective geometry. We design view-conditioned queries at the output level, which enables the generation of multiple virtual frames during training to learn viewpoint equivariance by enforcing multi-view consistency. The multi-view geometry injected at the input level as positional encodings and regularized at the loss level provides rich geometric cues for 3d object detection, leading to state-of-the-art performance on the nuScenes benchmark. The code and model are made available at https://***/TRI-ML/VEdet.

关键词： 3d from multi-view and sensors

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