Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar (2408.12191v4)

Abstract: We consider the problem of few-viewpoint 3D surface reconstruction using raw measurements from a lidar system. Lidar captures 3D scene geometry by emitting pulses of light to a target and recording the speed-of-light time delay of the reflected light. However, conventional lidar systems do not output the raw, captured waveforms of backscattered light; instead, they pre-process these data into a 3D point cloud. Since this procedure typically does not accurately model the noise statistics of the system, exploit spatial priors, or incorporate information about downstream tasks, it ultimately discards useful information that is encoded in raw measurements of backscattered light. Here, we propose to leverage raw measurements captured with a single-photon lidar system from multiple viewpoints to optimize a neural surface representation of a scene. The measurements consist of time-resolved photon count histograms, or transients, which capture information about backscattered light at picosecond time scales. Additionally, we develop new regularization strategies that improve robustness to photon noise, enabling accurate surface reconstruction with as few as 10 photons per pixel. Our method outperforms other techniques for few-viewpoint 3D reconstruction based on depth maps, point clouds, or conventional lidar as demonstrated in simulation and with captured data.

• The paper introduces Transientangelo, a novel approach that uses raw single-photon lidar measurements to optimize neural surface representations for 3D reconstruction.
• It employs innovative regularization techniques to achieve accurate surface modeling with as few as 10 photons per pixel, significantly reducing reconstruction errors.
• Extensive experiments in simulated and real-world scenarios demonstrate state-of-the-art performance in geometry accuracy and surface fidelity.

Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar

The paper "Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar," authored by Weihan Luo, Anagh Malik, and David B. Lindell, presents a novel approach to the problem of 3D surface reconstruction from limited viewpoints using single-photon lidar data. This work explores the utility of raw lidar measurements to produce high-fidelity surface reconstructions with minimal input data, ultimately advancing the techniques available for sparse-view and low-photon 3D reconstruction.

Key Contributions

1. Introduction of Transientangelo: The authors introduce Transientangelo, a method leveraging raw measurements from a single-photon lidar system to optimize a neural surface representation. This technique outperforms existing methods in few-viewpoint and low-photon regimes.
2. Regularization Techniques: Novel regularization strategies are developed to enhance robustness to photon noise, enabling accurate surface reconstructions with as few as 10 photons per pixel, significantly lowering the data requirements for high-quality 3D reconstructions.
3. Empirical Demonstrations: Extensive research and empirical data demonstrate superior performance in simulated and real-world conditions, establishing state-of-the-art results in both geometry accuracy and surface fidelity.

Methodology

Raw Measurement Utilization

Traditional lidar systems convert captured waveforms into standardized 3D point clouds, a process that loses significant raw data insights. In contrast, Transientangelo directly uses raw time-of-flight data encapsulated in photon count histograms. This method holds onto valuable, fine-grained temporal information that helps in achieving more robust geometric reconstructions, especially in scenarios with minimal data points.

Neural Surface Representation

The core innovation lies in using a neural surface representation parameterized by signed distance functions (SDFs). The SDF approach allows for precise surface modeling by defining surfaces where the SDF reaches zero. Leveraging advancements in neural representation technologies, such as hash-grid-based feature encodings and multi-layer perception networks (MLPs), the system efficiently translates photon arrival times into accurate 3D spatial data.

Transient Rendering and Optimization

Transient data from the lidar system is rendered using time-resolved volume rendering techniques. The authors' method incorporates photon count histograms directly into a rendering equation, which computes light transport by accounting for time and spatial variations. The optimization of the neural surface involves several loss functions:

• Transient Loss: Measures deviation between rendered transients and actual lidar readings.
• Weight Variance Regularization: Minimizes spurious surface artifacts by reducing variance in the weighted sum of voxel contributions along a ray.
• Reflectivity Loss: Employs integrated transients to provide additional supervision, enhancing performance in low-photon scenarios.

Evaluation and Results

Strong Numerical Results

The effectiveness of the proposed approach is substantiated through extensive experimental evaluations. The reconstructed meshes show significant improvements over existing baselines, with Chamfer Distance metrics indicating up to a five-fold reduction in errors compared to prior methods such as Neuralangelo, RegNeRF, and TransientNeRF. The method also performs consistently across various photon levels and different numbers of viewpoints, with tangible improvements in PSNR, LPIPS, and L1 depth metrics.

Real-World and Simulated Applications

The method's versatility and robustness are further demonstrated in both simulated and real-world test cases, maintaining performance even under challenging conditions of low photon counts and sparse viewpoints. Additionally, Transientangelo's ability to render transients with high fidelity suggests its potential for applications beyond conventional 3D modeling, including scenarios that necessitate rapid or long-range data acquisition.

Implications and Future Directions

Practical Applications: The improvements brought forth by Transientangelo are particularly relevant for fields like autonomous navigation, augmented reality, and digital heritage preservation, where high-fidelity reconstructions from limited data are crucial. The low-photon requirement makes the method viable for scenarios involving sensitive targets or extended range measurements.

Theoretical Enhancements: From a theoretical standpoint, the deployment of neural surface representations in association with transient lidar data opens new avenues for research in sensor fusion, where multiple modalities of data could be integrated to enhance 3D reconstruction fidelity further.

Future Prospects: Looking forward, extending these techniques using more advanced neural architectures (e.g., Gaussian splatting for efficient rendering) or combining them with newer sensing technologies could enhance both performance and applicability. Furthermore, incorporating global illumination models to account for indirect lighting would broaden the capacity for accurate scene reconstruction under diverse real-world conditions.

Conclusion

The paper introduces a sophisticated approach to 3D surface reconstruction using sparse and low-photon lidar data. By deploying neural surface representations and integrating raw lidar measurements, Transientangelo achieves state-of-the-art performance in various challenging scenarios, making significant contributions to the field of sparse-viewpoint 3D reconstruction. The presented techniques and findings not only enhance practical applications but also lay a robust groundwork for future explorations into more advanced neural and sensor integration methodologies.