Existing neural radiance fields (NeRF)-based novel view synthesis methods for
large-scale outdoor scenes are mainly built on a single altitude. Moreover,
they often require a priori camera shooting height and scene scope, leading to
inefficient and impractical applications when camera altitude changes. In this
work, we propose an end-to-end framework, termed AG-NeRF, and seek to reduce
the training cost of building good reconstructions by synthesizing
free-viewpoint images based on varying altitudes of scenes. Specifically, to
tackle the detail variation problem from low altitude (drone-level) to high
altitude (satellite-level), a source image selection method and an
attention-based feature fusion approach are developed to extract and fuse the
most relevant features of target view from multi-height images for
high-fidelity rendering. Extensive experiments demonstrate that AG-NeRF
achieves SOTA performance on 56 Leonard and Transamerica benchmarks and only
requires a half hour of training time to reach the competitive PSNR as compared
to the latest BungeeNeRF.

提出了一种名为 AG-NeRF 的端到端框架，通过合成根据不同高度的场景生成自由视角图像，从而降低构建良好重建模型的训练成本，并通过源图像选择方法和基于注意力的特征融合方法来解决从低高度到高高度的细节变化问题，实验证明 AG-NeRF 在多个评估基准上达到了 SOTA 水平，并且仅需半小时的训练时间与最新的 BungeeNeRF 相比便可达到竞争性的峰值信噪比。

AG-NeRF：用于多高度大规模室外场景渲染的注意力引导神经辐射场

AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height  Large-scale Outdoor Scene Rendering

This work targets at using a general deep learning framework to synthesize
free-viewpoint images of arbitrary human performers, only requiring a sparse
number of camera views as inputs and skirting per-case fine-tuning. The large
variation of geometry and appearance, caused by articulated body poses, shapes
and clothing types, are the key bottlenecks of this task. To overcome these
challenges, we present a simple yet powerful framework, named Generalizable
Neural Performer (GNR), that learns a generalizable and robust neural body
representation over various geometry and appearance. Specifically, we compress
the light fields for novel view human rendering as conditional implicit neural
radiance fields from both geometry and appearance aspects. We first introduce
an Implicit Geometric Body Embedding strategy to enhance the robustness based
on both parametric 3D human body model and multi-view images hints. We further
propose a Screen-Space Occlusion-Aware Appearance Blending technique to
preserve the high-quality appearance, through interpolating source view
appearance to the radiance fields with a relax but approximate geometric
guidance.
To evaluate our method, we present our ongoing effort of constructing a
dataset with remarkable complexity and diversity. The dataset GeneBody-1.0,
includes over 360M frames of 370 subjects under multi-view cameras capturing,
performing a large variety of pose actions, along with diverse body shapes,
clothing, accessories and hairdos. Experiments on GeneBody-1.0 and ZJU-Mocap
show better robustness of our methods than recent state-of-the-art
generalizable methods among all cross-dataset, unseen subjects and unseen poses
settings. We also demonstrate the competitiveness of our model compared with
cutting-edge case-specific ones. Dataset, code and model will be made publicly
available.

使用深度学习技术，提出了一种名为 GNR 的通用神经演员框架，通过压缩轻场数据来从几何和外观方面生成新颖的视角下的人体影像，并在多视角摄像机下构建的数据集 GeneBody-1.0 上证明了其鲁棒性和通用性。