PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar
(2312.14239)Abstract
3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF), while popular for view synthesis and 3D reconstruction, are typically reliant on multi-view images. Existing methods for single-view 3D reconstruction with NeRF rely on either data priors to hallucinate views of occluded regions, which may not be physically accurate, or shadows observed by RGB cameras, which are difficult to detect in ambient light and low albedo backgrounds. We propose using time-of-flight data captured by a single-photon avalanche diode to overcome these limitations. Our method models two-bounce optical paths with NeRF, using lidar transient data for supervision. By leveraging the advantages of both NeRF and two-bounce light measured by lidar, we demonstrate that we can reconstruct visible and occluded geometry without data priors or reliance on controlled ambient lighting or scene albedo. In addition, we demonstrate improved generalization under practical constraints on sensor spatial- and temporal-resolution. We believe our method is a promising direction as single-photon lidars become ubiquitous on consumer devices, such as phones, tablets, and headsets.
Overview
-
Introduces PlatoNeRF technique for 3D scene geometry reconstruction using single-view and two-bounce lidar signals.
-
Utilizes single-photon lidar transient data to train a neural network, overcoming challenges of existing single-view methods.
-
Achieves superior accuracy in reconstructing both visible and occluded areas compared to current single-view image or lidar methods.
-
Demonstrates robustness to practical constraints like sensor resolution and ambient lighting variations, suggesting usability in consumer devices.
-
Provides a novel dataset for further research and highlights potential improvements for handling non-Lambertian surfaces and artifacts.
Background
3D reconstruction, crucial for various fields like autonomous driving and virtual reality, traditionally depends on multi-view imagery to reconstruct scene geometry. However, obtaining multiple views can be tedious and impractical in dynamic environments. Current methods for single-view reconstruction either rely on imprecise data-driven estimation of occluded areas or struggle with ambient lighting and low albedo surfaces.
Approach and Methodology
This paper introduces a new technique, termed PlatoNeRF, for reconstructing 3D scene geometry using a single view by incorporating neural radiance fields (NeRF) and two-bounce lidar signals. This method leverages single-photon lidar transient data to supervise NeRF, overcoming the challenges faced by existing single-view methods. The research notably uses time-of-flight measurements gathered from light that has bounced twice within the scene, containing key information about both the visible geometry and occluded areas.
The process involves illuminating specific points in the scene with a laser, capturing how light reflects within the scene and then uses the sensor to measure both the direct and indirect light paths. A neural network is then trained to represent the scene's geometry based on these measurements.
Results and Analysis
The developed method outperforms existing techniques relying on single-view images or two-bounce lidar systems, providing more accurate reconstructions of both visible and hidden areas. It further showcases improved generalization under various practical constraints, such as reduced sensor resolution and different ambient light conditions. The results demonstrate the method’s robustness and its potential application in consumer devices with built-in lidar technology.
Contributions and Implications
The main contributions of this work include a novel two-bounce lidar and NeRF model, the capability for single-view 3D reconstruction without detailed scene information, and an in-depth analysis illustrating the method's resilience to various environmental factors. Additionally, the authors have generated a simulated dataset to facilitate further research and will make it available to the public. This innovation signifies a promising step forward, particularly as single-photon lidar sensors become more common in everyday devices. Future improvements may include better handling of non-Lambertian surfaces and the removal of occasional artifacts in reconstructions.
Create an account to read this summary for free: