2023-03-22 更新
DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields
Authors:Wei-Ting Chen, Wang Yifan, Sy-Yen Kuo, Gordon Wetzstein
Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction. However, these methods fail in adverse weather conditions. To address this challenge, we introduce DehazeNeRF as a framework that robustly operates in hazy conditions. DehazeNeRF extends the volume rendering equation by adding physically realistic terms that model atmospheric scattering. By parameterizing these terms using suitable networks that match the physical properties, we introduce effective inductive biases, which, together with the proposed regularizations, allow DehazeNeRF to demonstrate successful multi-view haze removal, novel view synthesis, and 3D shape reconstruction where existing approaches fail.
PDF including supplemental material; project page: https://www.computationalimaging.org/publications/dehazenerf
点此查看论文截图
ExtremeNeRF: Few-shot Neural Radiance Fields Under Unconstrained Illumination
Authors:SeokYeong Lee, JunYong Choi, Seungryong Kim, Ig-Jae Kim, Junghyun Cho
In this paper, we propose a new challenge that synthesizes a novel view in a more practical environment, where the number of input multi-view images is limited and illumination variations are significant. Despite recent success, neural radiance fields (NeRF) require a massive amount of input multi-view images taken under constrained illuminations. To address the problem, we suggest ExtremeNeRF, which utilizes occlusion-aware multiview albedo consistency, supported by geometric alignment and depth consistency. We extract intrinsic image components that should be illumination-invariant across different views, enabling direct appearance comparison between the input and novel view under unconstrained illumination. We provide extensive experimental results for an evaluation of the task, using the newly built NeRF Extreme benchmark, which is the first in-the-wild novel view synthesis benchmark taken under multiple viewing directions and varying illuminations. The project page is at https://seokyeong94.github.io/ExtremeNeRF/
PDF
点此查看论文截图
Real-time volumetric rendering of dynamic humans
Authors:Ignacio Rocco, Iurii Makarov, Filippos Kokkinos, David Novotny, Benjamin Graham, Natalia Neverova, Andrea Vedaldi
We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos with accompanying parametric body fits. Our method can reconstruct a dynamic human in less than 3h using a single GPU, compared to recent state-of-the-art alternatives that take up to 72h. These speedups are obtained by using a lightweight deformation model solely based on linear blend skinning, and an efficient factorized volumetric representation for modeling the shape and color of the person in canonical pose. Moreover, we propose a novel local ray marching rendering which, by exploiting standard GPU hardware and without any baking or conversion of the radiance field, allows visualizing the neural human on a mobile VR device at 40 frames per second with minimal loss of visual quality. Our experimental evaluation shows superior or competitive results with state-of-the art methods while obtaining large training speedup, using a simple model, and achieving real-time rendering.
PDF Project page: https://real-time-humans.github.io/
点此查看论文截图