Empowering Sparse-Input Neural Radiance Fields with Dual-Level Semantic Guidance from Dense Novel Views (2503.02230v1)

Abstract: Neural Radiance Fields (NeRF) have shown remarkable capabilities for photorealistic novel view synthesis. One major deficiency of NeRF is that dense inputs are typically required, and the rendering quality will drop drastically given sparse inputs. In this paper, we highlight the effectiveness of rendered semantics from dense novel views, and show that rendered semantics can be treated as a more robust form of augmented data than rendered RGB. Our method enhances NeRF's performance by incorporating guidance derived from the rendered semantics. The rendered semantic guidance encompasses two levels: the supervision level and the feature level. The supervision-level guidance incorporates a bi-directional verification module that decides the validity of each rendered semantic label, while the feature-level guidance integrates a learnable codebook that encodes semantic-aware information, which is queried by each point via the attention mechanism to obtain semantic-relevant predictions. The overall semantic guidance is embedded into a self-improved pipeline. We also introduce a more challenging sparse-input indoor benchmark, where the number of inputs is limited to as few as 6. Experiments demonstrate the effectiveness of our method and it exhibits superior performance compared to existing approaches.

• The paper proposes a novel method enhancing sparse-input Neural Radiance Fields (NeRF) performance by using dual-level semantic guidance from dense novel views.
• The method employs dual semantic guidance, including Bi-Directional Verification for label validity and a learnable codebook for semantic-aware feature encoding.
• Experiments on new benchmarks demonstrate the method achieves superior performance in sparse configurations, highlighting the effectiveness of semantic augmentation for robust NeRF.

Empowering Sparse-Input Neural Radiance Fields with Dual-Level Semantic Guidance

In the context of Neural Radiance Fields (NeRF), a significant limitation is their dependence on dense input data for high-quality photorealistic novel view synthesis. This paper addresses the challenge of sparse input scenarios, where NeRF struggles with shape-radiance ambiguity, resulting in suboptimal rendering quality. The authors propose a novel method that leverages dual-level semantic guidance derived from dense novel view renderings to enhance NeRF performance even with minimal input data.

The method integrates a semantic guidance mechanism operating on two levels: supervision and feature. At the supervision level, a Bi-Directional Verification module is introduced to assess the validity of each rendered semantic label. This module employs a projection-based consensus constraint to ensure that semantic labels hold across corresponding novel and source views. By verifying the accuracy of these labels, the training process of the student NeRF is guided more reliably, particularly in sparse-input scenarios.

At the feature level, the approach incorporates a learnable codebook within the Multi-Layer Perceptron (MLP) architecture. This codebook encodes semantic-aware information, capturing the correlations among color, semantics, and densities. The MLP queries this codebook using an attention mechanism, thereby guiding the training towards more accurate color and density predictions. This feature-level guidance complements the semantic supervision by further enhancing the cue-based understanding of the scene.

The proposed method establishes itself in the landscape of sparse-input NeRF methodologies by introducing a new benchmark using the ScanNet++ and Replica datasets. These benchmarks offer a more challenging setup with limited input views, testing the robustness and efficiency of various algorithms, including the one proposed.

Experiments demonstrate the effectiveness of the approach, as the proposed S$^3$NeRF (Dense Semantic Guidance for Neural Radiance Fields from Sparse Inputs with Self-Improvement) achieves superior performance metrics compared to existing methods. The introduction of semantic guidance shows significant improvements, notably in challenging sparse configurations, by resolving ambiguities that traditionally limit NeRF. The paper also explores the dependencies on input view density by evaluating performance under variable viewing conditions.

The paper's contribution is not just in solving an existing problem but also in providing a framework that paves the way for further exploration of semantic augmentation in NeRF. It suggests potential future research directions, including the refinement of semantic guidance mechanisms and the exploration of semantic correlations for richer, more informative scene understanding. This work underscores the importance and potential of semantics in bridging the gap between limited input data and high-quality 3D scene reconstruction, presenting a meaningful step forward in the development of robust view synthesis models.