NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior (2212.07388v3)

Abstract: Training a Neural Radiance Field (NeRF) without pre-computed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy. Our project page is https://nope-nerf.active.vision.

• The paper introduces a method that leverages monocular depth maps to jointly optimize camera poses and neural radiance fields, eliminating the need for pre-computed pose priors.
• It employs innovative loss functions like Chamfer Distance and surface rendering loss to ensure multi-view depth consistency and stable optimization.
• Experiments demonstrate improved novel view synthesis and superior pose estimation benchmarks in diverse real-world and synthetic scenes.

Overview of "NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior"

NoPe-NeRF addresses a pressing challenge in the field of Neural Radiance Fields (NeRFs) - the requirement of pre-computed camera poses for effective training. Traditional methods often resort to Structure-from-Motion (SfM) libraries like COLMAP to deduce these poses, but this process is computationally expensive and not differentiable, limiting its scalability and integration into end-to-end training pipelines. The paper proposes a novel method that fuses monocular depth priors with NeRF training to jointly optimize neural scene representations and camera poses without needing initial pose input.

Core Contributions and Methodology

The authors introduce an approach where monocular depth information is leveraged to overcome pose estimation challenges that commonly arise under significant camera movement. This depth information, corrected for scale and shift distortions, facilitates reliable relative pose computations between consecutive frames. The primary components of the method include:

1. Monocular Depth Integration: Unlike traditional approaches that employ multi-view stereo depth estimation, the method utilizes stereo-derived monocular depth maps. These are lightweight and do not require known camera parameters. The monocular depth maps are adjusted for scale and shift to create a multi-view consistent set of depth maps using NeRF's inherent multiview consistency during training.
2. Novel Loss Functions:
   • The depth maps, once consistent, are incorporated into the training via Chamfer Distance loss, which evaluates the consistency between depth maps of adjacent frames.
   • A depth-based surface rendering loss adds another layer of constraint to refine relative pose estimates.
3. Joint Pose and Radiance Field Optimization: The system combines the relative pose constraints and depth-informatics to stabilize the joint optimization of camera poses and the radiance field. This reduces ambiguities in the model, leading to faster convergence and increased stability across dramatic camera movements.

Experimental Results

Experiments showcase the efficacy of NoPe-NeRF against state-of-the-art baselines, both in synthetic and real-world environments such as Tanks and Temples and ScanNet. Key outcomes from these experiments are:

• Improved Novel View Synthesis: Compared to traditional methods that require pose initialization, NoPe-NeRF delivers superior render quality on account of enhanced depth guidance and accurate pose estimation, validated using PSNR, SSIM, and LPIPS metrics.
• Superior Pose Estimation: The method surpasses existing alternatives by a significant margin regarding pose accuracy. The error metrics ATE and RPE are notably lower, demonstrating robustness in diverse scene trajectories.

Implications and Future Directions

NoPe-NeRF advances the prospect of deploying NeRF models in scenarios where direct pose measurement is impractical. Its robust handling of monocular depth to guide pose estimation could have direct applications in virtual reality, autonomous vehicle navigation, and scene reconstruction. Beyond this immediate applicability, the introduction of more sophisticated monocular depth models and real-time optimization techniques is a likely future trajectory, promising further integration of neural representations with dynamic environment modeling. Future research might also explore cross-dataset generalization, enhancing the method's capacity for unsupervised domain adaptation.

Conclusively, the integration of monocular depth and novel pose constraints in NoPe-NeRF offers a viable and efficient alternative to traditional SfM-based NeRF pipelines, pointing towards a more flexible and scalable approach for scene synthesis and navigation.