Efficient Weakly Supervised LiDAR Semantic Segmentation using Foundation Model Assisted Sparse Annotations

Centrala begrepp

A novel weakly supervised learning approach that utilizes sparse image annotations and a foundation model to generate labels for 3D LiDAR point clouds, achieving high-performance semantic segmentation with limited point-level supervision.

Sammanfattning

This paper proposes a novel weakly supervised learning approach to tackle the expensive and time-consuming dense annotation of point clouds for semantic segmentation. The key aspects are:

Scatter-KITTI and Scatter-NuScenes datasets: The authors introduce two new datasets that utilize sparse annotations on 2D images to generate pseudo-labels for 3D LiDAR point clouds. This significantly reduces the annotation effort compared to fully supervised methods.

MM-ScatterNet architecture: The authors design a multimodal fusion network called MM-ScatterNet that integrates point cloud and image features. It employs a perceptual consistency loss to extract useful information from the image modality and enhance the representation learning of point clouds.

Experimental results: On the SemanticKITTI and NuScenes datasets, the proposed approach achieves 66% and 75% of the fully supervised performance, respectively, using only 0.02% and 0.1% of the labeled points. This demonstrates the effectiveness of the sparse annotation strategy and the MM-ScatterNet architecture.

The authors show that by leveraging abundant image data and a foundation model for semantic segmentation, they can generate high-quality pseudo-labels for 3D point clouds, significantly reducing the annotation burden. The MM-ScatterNet network then effectively fuses the point cloud and image features to mitigate the impact of erroneous pseudo-labels, leading to strong segmentation performance with limited supervision.

Statistik

Using only 0.02% of labeled points, the proposed approach achieves 66% of the fully supervised performance on the SemanticKITTI dataset.
Using only 0.1% of labeled points, the proposed approach achieves 75% of the fully supervised performance on the NuScenes dataset.

Citat

"We for the first time employ SAM [5] (a foundation model for image segmentation) to expand the original sparse annotations."
"By mapping the segmentation labels of the images to the LiDAR space using the intrinsic and extrinsic parameters of the camera and LiDAR, we obtain labels for point cloud semantic segmentation."
"To mitigate the influence of erroneous pseudo labels obtained from sparse annotations on point cloud features, we propose a multi-modal weakly supervised network for LiDAR semantic segmentation, called MM-ScatterNet."

Viktiga insikter från

Foundation Model assisted Weakly Supervised LiDAR Semantic Segmentation

by Yilong Chen,... på arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12861.pdf

Djupare frågor

How can the proposed approach be extended to handle dynamic scenes or handle occlusions in the LiDAR point clouds

The proposed approach can be extended to handle dynamic scenes or handle occlusions in LiDAR point clouds by incorporating temporal information and advanced processing techniques. For dynamic scenes, the system can utilize motion estimation algorithms to track moving objects across frames and update the semantic segmentation labels accordingly. This can involve techniques such as optical flow estimation or Kalman filtering to predict the movement of objects in the scene. Additionally, the system can leverage LiDAR intensity values to differentiate between static and dynamic objects, enabling the segmentation of dynamic elements in the scene.
To address occlusions in LiDAR point clouds, the system can employ advanced point cloud processing methods such as voxel-based or graph-based approaches. Voxel-based methods can aggregate information from neighboring points to infer the presence of occluded objects, while graph-based methods can model occlusions as edges in the graph structure and incorporate contextual information for accurate segmentation. Furthermore, the system can utilize multi-sensor fusion techniques, combining data from LiDAR with other sensors like cameras or radar to enhance the perception of occluded regions and improve segmentation accuracy in challenging scenarios.

What are the potential limitations of using a foundation model like SAM for generating pseudo-labels, and how can these be addressed

Using a foundation model like SAM for generating pseudo-labels may have limitations related to the quality and reliability of the generated labels. One potential limitation is the generalization capability of the foundation model, as it may not be trained on specific LiDAR data characteristics, leading to inaccuracies in pseudo-label generation. To address this limitation, fine-tuning the foundation model on LiDAR-specific datasets can improve its performance in generating accurate pseudo-labels for point cloud semantic segmentation tasks.
Another limitation could be the sensitivity of the foundation model to noise or outliers in the input data, which can result in erroneous pseudo-labels. Implementing data preprocessing techniques such as outlier removal, noise reduction, or data augmentation can help mitigate the impact of noisy input data on the pseudo-label generation process. Additionally, incorporating uncertainty estimation methods into the pseudo-label generation process can provide confidence scores for the generated labels, enabling the system to identify and filter out unreliable pseudo-labels.

How can the proposed framework be adapted to other 3D perception tasks beyond semantic segmentation, such as object detection or instance segmentation

The proposed framework can be adapted to other 3D perception tasks beyond semantic segmentation, such as object detection or instance segmentation, by modifying the network architecture and loss functions to suit the specific task requirements. For object detection, the system can incorporate region proposal networks (RPNs) and anchor-based detection heads to localize and classify objects in the point cloud. By integrating 3D bounding box regression and objectness prediction modules, the system can accurately detect and localize objects in the scene.
For instance segmentation, the framework can be extended to predict instance-specific masks for individual objects in the point cloud. This can involve incorporating instance segmentation heads into the network architecture, along with instance-aware feature encoding and decoding modules. By leveraging clustering algorithms or embedding space techniques, the system can differentiate between different instances of the same object class and generate instance-level segmentation masks for each object in the scene. Additionally, incorporating instance-aware loss functions and metrics can further improve the accuracy of instance segmentation results.