F$^{2}$-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories (2303.15951v1)

Abstract: This paper presents a novel grid-based NeRF called F2-NeRF (Fast-Free-NeRF) for novel view synthesis, which enables arbitrary input camera trajectories and only costs a few minutes for training. Existing fast grid-based NeRF training frameworks, like Instant-NGP, Plenoxels, DVGO, or TensoRF, are mainly designed for bounded scenes and rely on space warping to handle unbounded scenes. Existing two widely-used space-warping methods are only designed for the forward-facing trajectory or the 360-degree object-centric trajectory but cannot process arbitrary trajectories. In this paper, we delve deep into the mechanism of space warping to handle unbounded scenes. Based on our analysis, we further propose a novel space-warping method called perspective warping, which allows us to handle arbitrary trajectories in the grid-based NeRF framework. Extensive experiments demonstrate that F2-NeRF is able to use the same perspective warping to render high-quality images on two standard datasets and a new free trajectory dataset collected by us. Project page: https://totoro97.github.io/projects/f2-nerf.

• The paper introduces perspective warping to extend NeRF training to free camera trajectories in unbounded scenes.
• It leverages adaptive space subdivision and efficient hash-grid usage to improve scene reconstruction and optimize memory usage.
• Experimental results demonstrate high-quality image synthesis in 12 minutes on a 2080Ti GPU, outperforming traditional grid-based methods.

Overview of F$^{2}$-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories

The paper introduces F$^{2}$-NeRF, an innovative approach to Neural Radiance Fields (NeRFs) that facilitates rapid training and accommodates arbitrary camera trajectories. Traditional grid-based NeRF methods like Instant-NGP and TensoRF are constrained to specific scene types and require substantial pre-processing, particularly in handling unbounded scenes. F$^{2}$-NeRF presents a novel perspective warping technique, paving the way for handling diverse camera trajectories.

Core Contributions

1. Perspective Warping: The foundational contribution of this work is the introduction of perspective warping. This technique generalizes existing space-warping methods by utilizing a novel approach that permits efficient navigation across arbitrary camera trajectories in unbounded scenes. It effectively replaces earlier methods that were constrained to forward-facing and 360$^\circ$ object-centered views.
2. Adaptive Space Subdivision: F$^{2}$-NeRF leverages space subdivision strategies to allocate resources optimally across a scene. This adaptive approach mitigates issues related to uneven spatial representation, often observed in standard grid-based methodologies, enabling finer details in scene reconstruction.
3. Efficient Hash-Grid Usage: By utilizing multiple hash functions and a single hash table, F$^{2}$-NeRF preserves memory efficiency while allowing for the flexible representation of scenes with varied camera configurations. This reduces conflict in data representation, a common problem in multi-grid systems.
4. Perspective Sampling Strategy: The proposed approach includes a sampling mechanism that aligns with the perspective warping, enhancing rendering efficiency and fidelity by focusing computational resources where they are most needed.

Experimental Validation

The paper rigorously tests F$^{2}$-NeRF across multiple datasets, including a novel free trajectory dataset. Experimental results indicate that F$^{2}$-NeRF outperforms other state-of-the-art methods, such as Instant-NGP and DVGO, in scenarios where camera trajectories are complex or unbounded, achieving high-quality image synthesis rapidly—within 12 minutes on a 2080Ti GPU. In comparison, existing methods require more time or can't handle such flexible trajectories at all.

On standard datasets, F$^{2}$-NeRF demonstrates comparable or superior performance, indicating its robustness and adaptability to various scene alignments. The adaptability of perspective warping makes it advantageous over specific warping methods like NDC and inverse sphere warping, which are limited to particular scenarios.

Implications and Future Work

The implications of F$^{2}$-NeRF are significant for applications requiring rapid scene reconstruction and rendering, especially in dynamic environments with non-linear camera paths. It expands the potential for real-time applications in AR/VR, gaming, and virtual cinematography where such complex movements are common.

Future developments could explore integrating F$^{2}$-NeRF with larger-scale systems or more complex photometric challenges. Enhancements in multi-view consistency and further reducing computational load could also be desirable directions. Moreover, expanding the capabilities of perspective warping could potentially address remaining limitations in extremely challenging lighting or texture scenarios.

In conclusion, F$^{2}$-NeRF demonstrates a substantial advancement in training neural representations with flexibility in camera movements, achieving efficient, high-quality novel view synthesis. This contribution opens the door to a wider range of NeRF applications and sets a new standard for handling arbitrary trajectories within the field.