[Paper Review] NeuSample: Neural Sample Field for Efficient View Synthesis

Jiemin Fang, Lingxi Xie+4 more|arXiv (Cornell University)|Nov 30, 2021

3D Shape Modeling and Analysis44 references12 citations

TL;DR

NeuSample proposes a neural sample field that replaces NeRF's two-stage hierarchical sampling with a single-shot, lightweight network to map rays directly to optimized sampling distributions, achieving faster inference and improved rendering quality. The method reduces computational cost by eliminating coarse network inference while maintaining or exceeding NeRF's performance, with further acceleration via sample field extraction for efficient deployment.

ABSTRACT

Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy. To alleviate the burden, we delve into the coarse-to-fine, hierarchical sampling procedure of NeRF and point out that the coarse stage can be replaced by a lightweight module which we name a neural sample field. The proposed sample field maps rays into sample distributions, which can be transformed into point coordinates and fed into radiance fields for volume rendering. The overall framework is named as NeuSample. We perform experiments on Realistic Synthetic 360$^{\circ}$ and Real Forward-Facing, two popular 3D scene sets, and show that NeuSample achieves better rendering quality than NeRF while enjoying a faster inference speed. NeuSample is further compressed with a proposed sample field extraction method towards a better trade-off between quality and speed.

Motivation & Objective

• To address the high computational cost of NeRF's two-stage hierarchical sampling during inference.
• To improve rendering quality and speed by replacing the coarse sampling stage with a learnable, lightweight neural sample field.
• To enable efficient inference and model compression through a novel sample field extraction method.
• To achieve a better trade-off between rendering quality and speed without relying on caching or explicit data structures.

Proposed method

• The neural sample field is a 5D neural network that takes ray origin and direction as input and outputs N sampling distances along the ray.
• The sample field is trained end-to-end with the radiance field to optimize sampling distributions for high-quality volume rendering.
• The method eliminates the need for a separate coarse network by directly predicting optimized sample points in a single forward pass.
• A sample field extraction method is proposed to compress the network by pruning redundant points, reducing inference time by 25% while preserving quality.
• Position encoding with 10 frequencies is used to embed ray coordinates and directions into the sample field network.
• Volume rendering is performed using the predicted sample points and their corresponding radiance/opacity values from the radiance field.

Experimental results

Research questions

• RQ1 Can a learnable, single-shot neural sample field replace NeRF's two-stage hierarchical sampling to improve efficiency?
• RQ2 Does the proposed sample field lead to better rendering quality than conventional NeRF with reduced inference time?
• RQ3 Can the sample field be effectively compressed via extraction while maintaining high rendering quality?
• RQ4 How does the depth of the sample field network affect performance on scenes with varying complexity?
• RQ5 What is the impact of position encoding frequency on the sample field's performance?

Key findings

• NeuSample achieves superior rendering quality compared to NeRF on both Realistic Synthetic 360° and Real Forward-Facing benchmarks.
• The method reduces inference time by approximately 25% compared to standard NeRF, even without compression.
• On the synthetic lego scene, a 2-layer sample field network causes a PSNR drop of 4.36 dB, indicating deeper networks are essential for complex scenes.
• On the fern scene, reducing the sample field network depth from 8 to 2 layers results in only a 0.29 dB PSNR drop, showing greater robustness to depth reduction.
• Changing position encoding frequencies from 5 to 15 has minimal impact, with PSNR variation below 0.2 dB across both scenes.
• Sample visualization confirms that NeuSample produces more evenly distributed and strategically placed samples compared to NeRF’s dense clustering, which improves rendering accuracy.