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Kangle Deng
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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One common failure mode of Neural Radiance Field (NeRF) models is fitting incorrect geometries when given an insufficient number of input views. We propose DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning neural radiance fields that takes advantage of readily-available depth supervision. Our key insight is that sparse depth supervision can be used to regularize the learned geometry, a crucial component for effectively rendering novel views using NeRF. We exploit the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). Crucially, SFM also produces sparse 3D points that can be used as ``free depth supervision during training: we simply add a loss to ensure that depth rendered along rays that intersect these 3D points is close to the observed depth. We find that DS-NeRF can render more accurate images given fewer training views while training 2-6x faster. With only two training views on real-world images, DS-NeRF significantly outperforms NeRF as well as other sparse-view variants. We show that our loss is compatible with these NeRF models, demonstrating that depth is a cheap and easily digestible supervisory signal. Finally, we show that DS-NeRF supports other types of depth supervision such as scanned depth sensors and RGBD reconstruction outputs.
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We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multilayer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.
While deep learning has reshaped the classical motion capture pipeline, generative, analysis-by-synthesis elements are still in use to recover fine details if a high-quality 3D model of the user is available. Unfortunately, obtaining such a model for every user a priori is challenging, time-consuming, and limits the application scenarios. We propose a novel test-time optimization approach for monocular motion capture that learns a volumetric body model of the user in a self-supervised manner. To this end, our approach combines the advantages of neural radiance fields with an articulated skeleton representation. Our proposed skeleton embedding serves as a common reference that links constraints across time, thereby reducing the number of required camera views from traditionally dozens of calibrated cameras, down to a single uncalibrated one. As a starting point, we employ the output of an off-the-shelf model that predicts the 3D skeleton pose. The volumetric body shape and appearance is then learned from scratch, while jointly refining the initial pose estimate. Our approach is self-supervised and does not require any additional ground truth labels for appearance, pose, or 3D shape. We demonstrate that our novel combination of a discriminative pose estimation technique with surface-free analysis-by-synthesis outperforms purely discriminative monocular pose estimation approaches and generalizes well to multiple views.
Besides the COVID-19 pandemic and political upheaval in the US, 2020 was also the year in which neural volume rendering exploded onto the scene, triggered by the impressive NeRF paper by Mildenhall et al. (2020). Both of us have tried to capture this excitement, Frank on a blog post (Dellaert, 2020) and Yen-Chen in a Github collection (Yen-Chen, 2020). This note is an annotated bibliography of the relevant papers, and we posted the associated bibtex file on the repository.
We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x,y,z)$ and viewing direction $(theta, phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons.
The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different resolutions. The straightforward solution of supersampling by rendering with multiple rays per pixel is impractical for NeRF, because rendering each ray requires querying a multilayer perceptron hundreds of times. Our solution, which we call mip-NeRF (a la mipmap), extends NeRF to represent the scene at a continuously-valued scale. By efficiently rendering anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable aliasing artifacts and significantly improves NeRFs ability to represent fine details, while also being 7% faster than NeRF and half the size. Compared to NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with NeRF and by 60% on a challenging multiscale variant of that dataset that we present. Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.
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