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。