Neural Rendering representations have significantly contributed to the field
of 3D computer vision. Given their potential, considerable efforts have been
invested to improve their performance. Nonetheless, the essential question of
selecting training views is yet to be thoroughly investigated. This key aspect
plays a vital role in achieving high-quality results and aligns with the
well-known tenet of deep learning: "garbage in, garbage out". In this paper, we
first illustrate the importance of view selection by demonstrating how a simple
rotation of the test views within the most pervasive NeRF dataset can lead to
consequential shifts in the performance rankings of state-of-the-art
techniques. To address this challenge, we introduce a unified framework for
view selection methods and devise a thorough benchmark to assess its impact.
Significant improvements can be achieved without leveraging error or
uncertainty estimation but focusing on uniform view coverage of the
reconstructed object, resulting in a training-free approach. Using this
technique, we show that high-quality renderings can be achieved faster by using
fewer views. We conduct extensive experiments on both synthetic datasets and
realistic data to demonstrate the effectiveness of our proposed method compared
with random, conventional error-based, and uncertainty-guided view selection.

论文通过演示在最广泛的 NeRF 数据集中对测试视图进行简单旋转可以导致现有先进技术的性能排名发生重大偏移，阐明了视图选择的重要性。为了解决这一挑战，作者提出了一个统一的视图选择方法框架，并设计了全面的评估标准来评估其影响。通过集中关注重建对象的均匀视图覆盖，无需利用误差或不确定性估计，可以实现显著的改进，从而实现快速获得高质量的渲染效果。通过在合成数据集和现实数据上进行广泛实验，与随机选择、传统基于误差的选择和基于不确定性引导的选择相比，证明了所提方法的有效性。

NeRF Director：神经体积渲染中的视角选择再审视

NeRF Director: Revisiting View Selection in Neural Volume Rendering

Creating high-quality controllable 3D human models from multi-view RGB videos
poses a significant challenge. Neural radiance fields (NeRFs) have demonstrated
remarkable quality in reconstructing and free-viewpoint rendering of static as
well as dynamic scenes. The extension to a controllable synthesis of dynamic
human performances poses an exciting research question. In this paper, we
introduce a novel NeRF-based framework for pose-dependent rendering of human
performances. In our approach, the radiance field is warped around an SMPL body
mesh, thereby creating a new surface-aligned representation. Our representation
can be animated through skeletal joint parameters that are provided to the NeRF
in addition to the viewpoint for pose dependent appearances. To achieve this,
our representation includes the corresponding 2D UV coordinates on the mesh
texture map and the distance between the query point and the mesh. To enable
efficient learning despite mapping ambiguities and random visual variations, we
introduce a novel remapping process that refines the mapped coordinates.
Experiments demonstrate that our approach results in high-quality renderings
for novel-view and novel-pose synthesis.

基于 NeRF 的人体表演姿态依赖渲染的新方法，通过将辐射场围绕 SMPL 人体模型进行扭曲，实现了新的表面对齐表示，可以通过骨骼关节参数和视点来实现动画化，并通过引入新的重新映射过程来实现高质量渲染。