Expansive Supervision for Neural Radiance Field

TL;DR

This work tackles the computational bottleneck of Neural Radiance Field (NeRF) training by introducing Expansive Supervision, which selectively renders a small subset of pixels and expands their errors to estimate the full loss, leveraging a long-tail distribution of training errors correlated with image content. The method employs an anchor area extractor and a source area sampling strategy to form a supervision set $R'$, and defines an expansive loss $\hat{L}$ that guides training while reducing compute and memory, achieving up to 52% memory and 16% time savings with comparable rendering quality. It integrates with existing explicit caching acceleration frameworks and extends to Implicit Neural Representations for images, demonstrating broad applicability and strong early- and mid-training performance gains. Overall, Expansive Supervision offers a practical, scalable pathway to faster and more memory-efficient NeRF training for high-fidelity novel view synthesis.

Abstract

Neural Radiance Field (NeRF) has achieved remarkable success in creating immersive media representations through its exceptional reconstruction capabilities. However, the computational demands of dense forward passes and volume rendering during training continue to challenge its real-world applications. In this paper, we introduce Expansive Supervision to reduce time and memory costs during NeRF training from the perspective of partial ray selection for supervision. Specifically, we observe that training errors exhibit a long-tail distribution correlated with image content. Based on this observation, our method selectively renders a small but crucial subset of pixels and expands their values to estimate errors across the entire area for each iteration. Compared to conventional supervision, our approach effectively bypasses redundant rendering processes, resulting in substantial reductions in both time and memory consumption. Experimental results demonstrate that integrating Expansive Supervision within existing state-of-the-art acceleration frameworks achieves 52% memory savings and 16% time savings while maintaining comparable visual quality.

Figures (7)

• Figure 1: Overview of proposed method. Our approach adopts an expansive supervision technique to selectively render a subset of crucial pixels to estimate the error by expansive mechanism. Unlike conventional full supervision, which blindly renders all pixels, our method intelligently avoids redundant rendering processes, leading to significant reductions in training time and memory consumption.
• Figure 2: Motivational observation. (#2) The blue histogram shows the error distribution after 1000 iterations, revealing a pronounced long-tail characteristic. (#3) To enhance the visibility of error map, we transformed the data into a normal distribution, revealing correlations between redistributed errors and image content. (#4) The top 10% of errors identified during training are visualized, corresponding to regions with high-frequency details in the image content. (#5) The top 10% error map generated by our expansive supervision exhibits a high correlation with the actual error distribution.
• Figure 3: Pipeline of expansive supervision. The mechanism of expansive supervision is to exclusively render the crucial pixels, which consist of the pre-computed anchor area and sampled source areas, to estimate the loss. This estimation is accomplished through the expansive strategy described in Section \ref{['sec:supervision']}.
• Figure 4: Convergence performance of expansive supervision. Our method achieves precise error estimation comparable to full supervision and exhibits faster convergence as the number of supervised pixels increases.
• Figure 5: Visual quality comparison with standard supervision. Under the same constrained computational resources, expansive supervision demonstrates higher quality reconstruction compared to standard supervision.