Point-Based Neural Rendering with Per-View Optimization (2109.02369v2)

Abstract: There has recently been great interest in neural rendering methods. Some approaches use 3D geometry reconstructed with Multi-View Stereo (MVS) but cannot recover from the errors of this process, while others directly learn a volumetric neural representation, but suffer from expensive training and inference. We introduce a general approach that is initialized with MVS, but allows further optimization of scene properties in the space of input views, including depth and reprojected features, resulting in improved novel-view synthesis. A key element of our approach is our new differentiable point-based pipeline, based on bi-directional Elliptical Weighted Average splatting, a probabilistic depth test and effective camera selection. We use these elements together in our neural renderer, that outperforms all previous methods both in quality and speed in almost all scenes we tested. Our pipeline can be applied to multi-view harmonization and stylization in addition to novel-view synthesis.

• The paper introduces a hybrid neural rendering method combining differentiable point splatting and per-view optimization for enhanced image synthesis.
• It employs a probabilistic depth test to resolve visibility ambiguities and a camera selection algorithm to maximize novel view coverage.
• Experimental results demonstrate improved rendering fidelity and speed, addressing challenges in complex scenes with intricate structures.

Point-Based Neural Rendering with Per-View Optimization

The paper "Point-Based Neural Rendering with Per-View Optimization" introduces an innovative approach to neural rendering, addressing the limitations observed in previous methods that rely on either multi-view stereo (MVS) reconstructed geometry or volumetric neural representation. The authors propose a hybrid pipeline that leverages differentiable point-based techniques paired with per-view optimization to enhance image synthesis quality and computational efficiency.

Overview of the Approach

The paper outlines a methodology initialized with MVS geometry, allowing further refinement of scene attributes such as depth and latent features in the space of input views. This hybridizes traditional stability with neural flexibility, facilitating an optimization process that mitigates depth inaccuracies and errors for novel-view synthesis. The authors focus on three key components:

1. Differentiable Point Splatting: Utilizing bi-directional Elliptical Weighted Average (EWA) filtering, the process enables seamless projection and reprojection of points between input views and a synthesized novel view. This bidirectional point-based approach is pivotal for accurately handling perspective and depth while maintaining differentiability crucial for neural optimization.
2. Probabilistic Depth Test: This component resolves visibility ambiguities between overlapping splats from different input views. By modeling depth uncertainty within a probabilistic framework, the reliability of depth selection is enhanced, circumventing errors from traditional depth tests under MVS inaccuracies.
3. Camera Selection Algorithm: The selection process is inspired by maximum coverage set problems, optimizing input view aligning to maximize coverage in the novel view. This selection is integral for efficient rendering, especially in datasets with broad baselines.

Numerical Results and Claims

The authors demonstrate that their pipeline outshines prior methods in both speed and image quality across diverse tested scenes. This is attributed to the integrated design allowing joint optimization of per-view attributes and neural rendering components. Experimental results underscore significant improvements in synthesis fidelity, particularly in challenging scenarios involving complex structures such as vegetation and thin objects.

Implications and Future Directions

On the theoretical frontier, the introduction of differentiable point-based methods into neural rendering broadens the potential for fine-grained optimization directly in image space. Practically, the framework's success suggests its applicability extends beyond view synthesis, potentially impacting areas like multi-view harmonization and stylization.

Future developments may concentrate on enhancing handling of large-scale reconstruction errors, and integrating capabilities to address reflections and transparency through additional depth layers. Multi-view matting emerges as a promising application domain as well.

Conclusion

The proposed method effectively bridges the gap between stable global geometry and flexible image-based optimization, marking a significant advancement in neural rendering. The work lays foundational insights for evolving neural rendering techniques, inviting further inquiry into extending these principles to broader computational photography applications.

Overall, this paper is a significant contribution to the neural rendering domain, offering guiding principles for merging traditional and neural methodologies in multi-view imaging contexts.