DeepLiDAR: Accurate Outdoor Depth Prediction with Sparse LiDAR and Color Images

DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scenes

This paper introduces DeepLiDAR, a novel deep learning architecture designed to achieve accurate dense depth prediction for outdoor scenes using sparse LiDAR data and a single color image. The approach is inspired by successful indoor depth completion methods and leverages surface normals as an intermediate representation to generate dense depth maps. The network is trained end-to-end and features a modified encoder-decoder structure that efficiently fuses information from both the color image and the sparse LiDAR depth data.

Addressing Outdoor Challenges

DeepLiDAR tackles specific challenges encountered in outdoor environments. A key innovation is the prediction of a confidence mask, which helps manage mixed LiDAR signals that can occur near foreground boundaries due to occlusions. Furthermore, the network intelligently combines estimates derived from the color image and surface normals using learned attention maps. This mechanism is crucial for improving depth accuracy, particularly in distant areas of the scene.

Network Architecture and Training

The core of DeepLiDAR is its encoder-decoder architecture, which is adept at processing and integrating multi-modal data. The encoder extracts features from both the color image and the sparse depth map, while the decoder reconstructs the dense depth map. The intermediate prediction of surface normals guides the depth estimation process, providing a more robust representation of the scene's geometry. The entire network is trained in an end-to-end fashion, allowing for direct optimization of the depth prediction task.

Key Contributions and Results

Surface Normal Guidance: The use of surface normals as an intermediate representation significantly enhances depth prediction accuracy.
Fusion of Color and LiDAR Data: An effective mechanism for fusing color images and sparse LiDAR depth data is proposed.
Confidence Mask for Occlusions: A confidence mask is introduced to handle challenging scenarios like occlusions and boundary effects in outdoor scenes.
Attention-based Fusion: Learned attention maps improve the integration of different data sources, especially for distant objects.
State-of-the-Art Performance: Extensive experiments on the KITTI depth completion benchmark demonstrate that DeepLiDAR surpasses existing state-of-the-art methods.
Ablation Studies: Detailed ablation studies validate the positive impact of each component of the proposed model on the overall performance.
Generalization: The model shows good generalization capabilities, performing well even with input data exhibiting higher sparsity or originating from indoor scenes.

Technical Details

The paper details the network's architecture, including the specific layers and operations used in the encoder and decoder. It also discusses the loss functions employed during training, which typically include terms for depth prediction, surface normal consistency, and potentially other regularization terms. The integration of the confidence mask and attention mechanisms is explained in depth, highlighting how they contribute to the model's robustness and accuracy.

Applications and Future Work

DeepLiDAR has significant implications for various applications requiring accurate 3D scene understanding, such as autonomous driving, robotics, augmented reality, and 3D mapping. The ability to generate dense depth from sparse LiDAR and a single image is particularly valuable in scenarios where full LiDAR coverage is unavailable or cost-prohibitive. Future work could explore extending the architecture to handle dynamic scenes, incorporate other sensor modalities, or improve performance in adverse weather conditions.

Conclusion

DeepLiDAR represents a significant advancement in depth estimation from sparse sensor data. By effectively combining deep learning, surface normal prediction, and attention mechanisms, the model achieves high accuracy and robustness in challenging outdoor environments. Its strong performance on benchmark datasets underscores its potential for real-world applications.

Publication Details:

Conference: 2019 Conference on Computer Vision and Pattern Recognition (CVPR)
Date: April 2019
Organized by: IEEE
Links:

Research Areas:

Computer Vision

Research Labs:

Spatial AI Lab – Zurich