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Depth-Guided Robust and Fast Point Cloud Fusion NeRF for Sparse Input Views (2403.02063v1)

Published 4 Mar 2024 in cs.CV

Abstract: Novel-view synthesis with sparse input views is important for real-world applications like AR/VR and autonomous driving. Recent methods have integrated depth information into NeRFs for sparse input synthesis, leveraging depth prior for geometric and spatial understanding. However, most existing works tend to overlook inaccuracies within depth maps and have low time efficiency. To address these issues, we propose a depth-guided robust and fast point cloud fusion NeRF for sparse inputs. We perceive radiance fields as an explicit voxel grid of features. A point cloud is constructed for each input view, characterized within the voxel grid using matrices and vectors. We accumulate the point cloud of each input view to construct the fused point cloud of the entire scene. Each voxel determines its density and appearance by referring to the point cloud of the entire scene. Through point cloud fusion and voxel grid fine-tuning, inaccuracies in depth values are refined or substituted by those from other views. Moreover, our method can achieve faster reconstruction and greater compactness through effective vector-matrix decomposition. Experimental results underline the superior performance and time efficiency of our approach compared to state-of-the-art baselines.

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Citations (4)
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Summary

  • The paper introduces a novel depth-guided fusion NeRF that integrates point cloud fusion within a voxel grid to handle sparse input views and mitigate depth errors.
  • It maps 2D pixels into a 3D space and employs vector-matrix decomposition to significantly reduce reconstruction time and model size.
  • The proposed method outperforms state-of-the-art techniques by delivering enhanced rendering quality and improved time efficiency in novel-view synthesis.

Depth-Guided Robust and Fast Point Cloud Fusion NeRF for Sparse Input Views

Introduction

Neural Radiance Fields (NeRFs) have risen to prominence in the sphere of novel-view synthesis, which holds significance for applications such as AR/VR and autonomous driving. Traditional NeRF frameworks typically require a plethora of images from diverse views to facilitate effective training. This paper introduces an innovative depth-guided robust and fast point cloud fusion NeRF aimed at addressing the challenges posed by sparse input views. By integrating depth information to construct point clouds for each input view and fusing them to represent the scene, this approach not only refines inaccuracies in depth values but also enhances the compactness and reconstruction speed of the model.

Depth-Aware NeRFs for Sparse Inputs

Earlier attempts at integrating depth information into NeRFs for sparse inputs have seen limited success, mainly due to the ignorance of inaccuracies in depth maps and general inefficiency in reconstruction speed. These methods either directly use depth information as supervision, risking the introduction of inaccuracies, or intermittently rely on depth completion networks, which may further compromise the integrity of depth values. This research tackles such shortcomings by proposing a method where point clouds, constructed from each input view and characterized within a voxel grid, are meticulously accumulated to represent the entire scene in a much more accurate and time-efficient manner.

Methodology

The proposed method perceives radiance fields as an explicit voxel grid of features, a first of its kind integration of point cloud fusion with NeRF volumetric rendering. Through a meticulous process involving the mapping of 2D pixels into 3D space, construction of individual point clouds for each view, and their subsequent fusion to model the scene, inaccuracies in depth values are effectively refined or replaced. Additionally, this method significantly reduces the model's size and reconstruction time by employing efficient vector-matrix decomposition techniques for scene representation.

Contributions

This pioneering research makes several significant contributions to the field of NeRF and novel-view synthesis with sparse inputs:

  • Introduces a novel depth-guided robust and fast point cloud fusion NeRF that minimizes the impact of inaccurate depth values.
  • Proposes a unique integration of point cloud fusion into the NeRF framework, offering a novel NeRF scene representation strategy.
  • Demonstrates superior results in time efficiency and rendering quality compared to state-of-the-art methods.

Limitations and Future Work

Despite its achievements, this approach has limitations that warrant further investigation. The method's reliance on depth information and its representation through matrices and vectors opens avenues for exploring how these aspects can be further leveraged or optimized. Future work could focus on enhancing the NeRF performance and reconstruction speed by exploiting depth information and tensorial structures more effectively.

Conclusion

By addressing the limitations of existing depth-aware NeRFs for sparse input views and introducing an efficient point cloud fusion technique, this research significantly advances the field. It lays a foundation for future efforts aimed at optimizing NeRF frameworks for real-world applications requiring sparse inputs, promising improvements in both the fidelity of novel-view synthesis and the practical applicability of NeRF technologies.

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