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Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation

Vivek Anand, Bharat Lohani, Gaurav Pandey, Rakesh Mishra

TL;DR

LiDAR-intensity simulation is difficult because material properties and surface interactions are not readily available, and incidence angle plays a critical role in reflectance. The authors propose a physics-aware, incidence-angle–augmented deep learning framework that projects 3D LiDAR data into spherical images and evaluates U-NET and Pix2Pix on SemanticKITTI and VoxelScape, using multiple modalities. They show that including the incidence angle improves prediction accuracy for both architectures, with Pix2Pix outperforming U-NET due to adversarial training. The findings advance realistic LiDAR-intensity synthesis for autonomous driving perception and downstream tasks, enabling better training and evaluation of perception algorithms, by producing more faithful intensity predictions and enabling richer synthetic data generation.

Abstract

Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment understanding and navigation. LiDAR intensity provides valuable information about the reflected laser signals and plays a crucial role in enhancing the perception capabilities of AVs. However, accurately simulating LiDAR intensity remains a challenge due to the unavailability of material properties of the objects in the environment, and complex interactions between the laser beam and the environment. The proposed method aims to improve the accuracy of intensity simulation by incorporating physics-based modalities within the deep learning framework. One of the key entities that captures the interaction between the laser beam and the objects is the angle of incidence. In this work we demonstrate that the addition of the LiDAR incidence angle as a separate input to the deep neural networks significantly enhances the results. We present a comparative study between two prominent deep learning architectures: U-NET a Convolutional Neural Network (CNN), and Pix2Pix a Generative Adversarial Network (GAN). We implemented these two architectures for the intensity prediction task and used SemanticKITTI and VoxelScape datasets for experiments. The comparative analysis reveals that both architectures benefit from the incidence angle as an additional input. Moreover, the Pix2Pix architecture outperforms U-NET, especially when the incidence angle is incorporated.

Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation

TL;DR

LiDAR-intensity simulation is difficult because material properties and surface interactions are not readily available, and incidence angle plays a critical role in reflectance. The authors propose a physics-aware, incidence-angle–augmented deep learning framework that projects 3D LiDAR data into spherical images and evaluates U-NET and Pix2Pix on SemanticKITTI and VoxelScape, using multiple modalities. They show that including the incidence angle improves prediction accuracy for both architectures, with Pix2Pix outperforming U-NET due to adversarial training. The findings advance realistic LiDAR-intensity synthesis for autonomous driving perception and downstream tasks, enabling better training and evaluation of perception algorithms, by producing more faithful intensity predictions and enabling richer synthetic data generation.

Abstract

Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment understanding and navigation. LiDAR intensity provides valuable information about the reflected laser signals and plays a crucial role in enhancing the perception capabilities of AVs. However, accurately simulating LiDAR intensity remains a challenge due to the unavailability of material properties of the objects in the environment, and complex interactions between the laser beam and the environment. The proposed method aims to improve the accuracy of intensity simulation by incorporating physics-based modalities within the deep learning framework. One of the key entities that captures the interaction between the laser beam and the objects is the angle of incidence. In this work we demonstrate that the addition of the LiDAR incidence angle as a separate input to the deep neural networks significantly enhances the results. We present a comparative study between two prominent deep learning architectures: U-NET a Convolutional Neural Network (CNN), and Pix2Pix a Generative Adversarial Network (GAN). We implemented these two architectures for the intensity prediction task and used SemanticKITTI and VoxelScape datasets for experiments. The comparative analysis reveals that both architectures benefit from the incidence angle as an additional input. Moreover, the Pix2Pix architecture outperforms U-NET, especially when the incidence angle is incorporated.
Paper Structure (15 sections, 2 equations, 7 figures, 1 table)

This paper contains 15 sections, 2 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Simulation for Autonomous Vehicles
  4. Physics-based LiDAR Simulation
  5. Learning-based LiDAR Simulation
  6. Method
  7. Network Architecture
  8. U-NET Architecture
  9. Pix2Pix Architecture
  10. Results and Discussion
  11. Mean Squared Error (MSE)
  12. Error Histogram
  13. Error Heatmaps
  14. Qualitative Analysis
  15. Conclusion

Figures (7)

  • Figure 1: Data Preparation: The LiDAR point cloud is projected on a spherical surface to create LiDAR spherical projection images
  • Figure 2: Incidence Angle Calculation:(a) LiDAR point cloud (b) Estimating surface normal (c) Orienting surface normal towards the sensor (d) Computing the direction vector of LiDAR rays (e) Computing the dot product between the direction and normal vectors of the point to get the incidence angle
  • Figure 3: Training pipeline: LiDAR Spherical images are fed into the architecture to predict LiDAR intensity.
  • Figure 4: Error Histogram: SemanticKITTI Data - Input combinations: (a) Depth + RGB + Label (b) Depth + RGB (c) Depth
  • Figure 5: Error Histogram: VoxelScape Data - Input combinations: (a) Depth + Label (b) Depth
  • ...and 2 more figures