Label-efficient Semantic Scene Completion with Scribble Annotations (2405.15170v1)

Abstract: Semantic scene completion aims to infer the 3D geometric structures with semantic classes from camera or LiDAR, which provide essential occupancy information in autonomous driving. Prior endeavors concentrate on constructing the network or benchmark in a fully supervised manner. While the dense occupancy grids need point-wise semantic annotations, which incur expensive and tedious labeling costs. In this paper, we build a new label-efficient benchmark, named ScribbleSC, where the sparse scribble-based semantic labels are combined with dense geometric labels for semantic scene completion. In particular, we propose a simple yet effective approach called Scribble2Scene, which bridges the gap between the sparse scribble annotations and fully-supervision. Our method consists of geometric-aware auto-labelers construction and online model training with an offline-to-online distillation module to enhance the performance. Experiments on SemanticKITTI demonstrate that Scribble2Scene achieves competitive performance against the fully-supervised counterparts, showing 99% performance of the fully-supervised models with only 13.5% voxels labeled. Both annotations of ScribbleSC and our full implementation are available at https://github.com/songw-zju/Scribble2Scene.

• The paper presents Scribble2Scene, which leverages geometry-aware auto-labelers and range-guided distillation to achieve semantic scene completion with sparse scribble annotations.
• It demonstrates up to 99% performance compared to fully-supervised models while using only 13.5% of the typical labeled data.
• This approach significantly reduces annotation effort, paving the way for scalable 3D scene understanding in applications like autonomous driving.

An Analysis of "Label-efficient Semantic Scene Completion with Scribble Annotations"

Semantic Scene Completion (SSC) is pivotal in tasks such as autonomous driving, where understanding and interpreting 3D environments is crucial. The paper "Label-efficient Semantic Scene Completion with Scribble Annotations" proposes an innovative approach aimed at reducing the annotation burden in SSC by utilizing sparse scribble annotations instead of traditional dense labels.

Methodological Framework

The heart of the method, dubbed Scribble2Scene, leverages both scribble annotations and dense geometric labels from raw LiDAR or camera data. Two major components define this approach: geometry-aware auto-labelers and an online model training with distillation strategy.

1. Geometry-aware Auto-labelers (GA$^2$L): The authors introduce Dean-Labeler and Teacher-Labeler within the GA$^2$L. Dean-Labeler treats the complete geometric structure as an entry to simplify the semantic segmentation task. The novelty here is in treating the task as if full semantic data were available, yet relying only on complete geometric input. Meanwhile, Teacher-Labeler is equipped with both the image and geometric data to provide high-quality proxy labels used in downstream training.
2. Online Model Training with Distillation (RGO$^2$D): This component efficiently bridges the offline, geometry-full, training to the online geometry-sparse application. The authors propose a range-guided distillation mechanism to ensure that knowledge effectively transfers from the elaborately trained Teacher-Labeler to the practical, online model.

Experimental Evaluation

Experimentation is a bedrock of this research. The authors validate their approach on challenging datasets such as SemanticKITTI and SemanticPOSS, demonstrating the efficacy with results reaching up to 99% performance of fully-supervised counterparts while using only 13.5% of the labeled data. These results are significant given the efficiency in label usage, signaling a substantial advancement over traditional methods demanding exhaustive manual labeling.

Practical and Theoretical Implications

From a practical standpoint, reducing the labeling requirement without a significant drop in performance has broad implications for deploying SSC systems in real-world scenarios where dense data labeling is often prohibitively costly. This approach can be particularly relevant in environments that rapidly evolve or in geographies with fewer labeling resources.

Theoretically, this work raises intriguing questions about the limits of label efficiency in other vision domains. It suggests potential for similar paradigms in areas like visual question answering or real-time object detection, pushing towards models that rely more on inferred or weakly-supervised data sources.

Future Directions

While Scribble2Scene showcases impressive results, there is room to expand this research. Future work could explore:

• Adapting the methodology for entirely label-free systems using self-supervised learning;
• Extending the approach to more complex scenes and smaller object classes, which pose persistent challenges;
• Investigating hierarchical distillation strategies that leverage multiple levels of representation from the teacher models.

In conclusion, this paper delineates a robust strategy for SSC, markedly reducing the need for expensive labels without forgoing accuracy, thereby contributing a significant stride towards practical, scalable 3D scene understanding in dynamic environments.