NeRF Director: Revisiting View Selection in Neural Volume Rendering

TL;DR

This paper highlights the critical impact of view selection in neural volume rendering, proposing a training-free, coverage-based method that improves rendering quality and efficiency without relying on error estimation.

Contribution

It introduces a unified framework and benchmark for view selection in neural rendering, emphasizing uniform coverage and demonstrating significant improvements over traditional methods.

Findings

View selection significantly affects performance rankings.

Coverage-based view selection enhances rendering quality.

Fewer views suffice for high-quality results.

Abstract

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.