LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes (2405.00900v2)

Abstract: Photorealistic simulation plays a crucial role in applications such as autonomous driving, where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However, reconstruction quality suffers on street scenes due to largely collinear camera motions and sparser samplings at higher speeds. On the other hand, the application often demands rendering from camera views that deviate from the inputs to accurately simulate behaviors like lane changes. In this paper, we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes. First, our framework learns a geometric scene representation from Lidar, which is fused with the implicit grid-based representation for radiance decoding, thereby supplying stronger geometric information offered by explicit point cloud. Second, we put forth a robust occlusion-aware depth supervision scheme, which allows utilizing densified Lidar points by accumulation. Third, we generate augmented training views from Lidar points for further improvement. Our insights translate to largely improved novel view synthesis under real driving scenes.

• The paper demonstrates DiL-NeRF's integration of Lidar data with NeRF, significantly improving 3D scene reconstruction for dynamic street environments.
• It introduces a robust method to densify sparse Lidar points and applies occlusion-aware depth supervision to enhance view extrapolation.
• Quantitative evaluations show improved PSNR and SSIM metrics, underpinning its potential for safer, more reliable autonomous driving simulations.

Enhancing NeRF with Lidar for Realistic Street Scene Rendering

Introduction to NeRF and its Challenges in Street Scenes

Neural Radiance Fields (NeRF) have transformed the way we create photorealistic simulations by efficiently synthesizing novel views of complex scenes using a neural network. Despite its popularity in controlled settings, using NeRF to simulate dynamic street scenes, especially for applications like autonomous driving, presents unique challenges:

• Collinear Camera Movements: Typically, street scene data is captured from vehicles moving primarily forward, resulting in collinear camera movement. This limits the available geometric information crucial for 3D reconstruction.
• Sparse Sampling at Higher Speeds: At higher driving speeds, fewer images are captured per unit of distance, leading to sparser data and, consequently, lower reconstruction quality.
• Demand for Off-trajectory Views: Simulating maneuvers like lane changes requires views that deviate significantly from the captured trajectories, demanding more effective view extrapolation capabilities from NeRF.

Addressing NeRF Challenges with Lidar in DiL-NeRF

The paper introduces DiL-NeRF, a framework that richly integrates Lidar data to address the limitations of applying NeRF in dynamic street scenes. Here's how DiL-NeRF tackles these challenges:

1. Lidar-Enhanced Geometric Scene Representation:
   • DiL-NeRF utilizes a geometric representation learned from Lidar data. By combining this with a grid-based radiance field, the model gains a stronger geometric understanding from the explicit point cloud data.
2. Robust Occlusion-aware Depth Supervision:
   • To combat the sparsity of Lidar, DiL-NeRF densifies Lidar points by aggregating data across frames, creating denser depth maps. A robust depth supervision mechanism filters out unreliable, occluded depth information throughout training.
3. Augmented Training Views from Lidar Points:
   • DiL-NeRF synthesizes additional training views by projecting accumulated Lidar points into novel viewpoints. Although this introduces potential occlusions, the robust supervision technique is used to mitigate this issue.

Performance and Evaluation

The adaptation of Lidar data in DiL-NeRF facilitates significant improvements in rendering quality, particularly under challenging real-world conditions. In quantitative evaluations, DiL-NeRF demonstrates improved performance metrics such as PSNR and SSIM across various street scenes when compared to existing methods like UniSim.

Implications and Future Directions

• Practical Implications: By enhancing NeRF's ability to utilize Lidar data effectively, DiL-NeRF paves the way for more accurate and reliable simulations in autonomous driving, where handling dynamic and complex street scenes is crucial for training and testing autonomous systems.
• Theoretical Implications: This approach pushes the boundaries of integrating explicit geometric data (from Lidar) with implicit models (like NeRF), which could lead to further research in hybrid modeling techniques in computer vision and graphics.
• Future Developments: Exploring the integration of dynamic object handling within the DiL-NeRF framework could further enhance its utility and applicability in real-world scenarios.

Conclusion

DiL-NeRF marks a significant step towards resolving the specific challenges of applying NeRF technology to dynamic street scenes, leveraging the depth and geometric precision of Lidar data to enhance the quality and reliability of photorealistic simulations. This advancement opens new possibilities for safely and efficiently training and testing autonomous vehicles in simulated environments that closely mimic the complexities of real-world driving.