Content-Aware Radiance Fields: Aligning Model Complexity with Scene Intricacy Through Learned Bitwidth Quantization

TL;DR

This paper proposes content-aware radiance fields, aligning the model complexity with the scene intricacies through Adversarial Content-Aware Quantization (A-CAQ), and makes the bitwidth of parameters differentiable and trainable, tailored to the unique characteristics of specific scenes and requirements.

Abstract

The recent popular radiance field models, exemplified by Neural Radiance Fields (NeRF), Instant-NGP and 3D Gaussian Splatting, are designed to represent 3D content by that training models for each individual scene. This unique characteristic of scene representation and per-scene training distinguishes radiance field models from other neural models, because complex scenes necessitate models with higher representational capacity and vice versa. In this paper, we propose content-aware radiance fields, aligning the model complexity with the scene intricacies through Adversarial Content-Aware Quantization (A-CAQ). Specifically, we make the bitwidth of parameters differentiable and trainable, tailored to the unique characteristics of specific scenes and requirements. The proposed framework has been assessed on Instant-NGP, a well-known NeRF variant and evaluated using various datasets. Experimental results demonstrate a notable reduction in computational complexity, while preserving the requisite reconstruction and rendering quality, making it beneficial for practical deployment of radiance fields models. Codes are available at https://github.com/WeihangLiu2024/Content_Aware_NeRF.

Figures (10)

• Figure 1: Overview of content-aware radiance fields. In this work, model complexity is aligned with scene intricacy through learned bitwidth quantization.
• Figure 2: The insights of the proposed LBQ. (a) The correlation between quantization sensitivity and scene complexity measured using average image gradient of the training set. Scenes with complex (simple) geometry and texture suffer more (less) accuracy degradation from quantization. (b) Exhibits the notable distinction of variables' distribution among different components. Those distributed in large (small) range is required to be quantized with high (low) bitwidth.
• Figure 3: The insights of the proposed A-CAQ. (a) Layer-wise quantization results for different scenes with various bitwidths, which reveals content-aware characteristics of quantization effects. (b) Results of layer-wise and non layer-wise quantization for the "lego" scene. The huge accuracy gap verifies the significance of mixed-precision models. (c) QAT alleviates performance degradation as quantization error expands.
• Figure 4: Overview of the LBQ quantization framework. The fake quantizers are inserted into different components including encoding and MLPs, which are parameterized with variable range scale, upper bound and bitwidth. Quantization error is simulated by quantize and de-quantize procedure and quantization parameters are updated with direction indicated by gradient descent. Examples with different complexity are given on the right.
• Figure 5: Qualitative results of the "lego" and "ship" datasets from Synthetic-NeRF. Proposed A-CAQ outputs visual clean results for both MDL and MGL scenarios.