Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes

Instance segmentation is a computer vision task that involves identifying and labeling objects in an image or point cloud. Weakly supervised instance segmentation is when the model is trained using simpler annotations, such as image-level labels, points, scribble, or boxes, instead of precise mask annotations. This article provides an overview of recent works on weakly supervised instance segmentation, highlighting the progress made possible by large-scale datasets like KITTI, nuScenes, and Waymo.
The authors explain that traditional instance segmentation methods rely heavily on manual annotation, which is time-consuming and expensive to produce high-quality labels. Weakly supervised learning offers a way to train models using easier-to-obtain annotations, reducing the need for manual labor. They also discuss how weakly supervised instance segmentation can be applied to various tasks, including semantic segmentation, instance segmentation, and object detection.
To better understand the concept of weakly supervised learning, the authors use an analogy of cooking a recipe. Imagine you have a recipe book with only rough guidelines on how to prepare a dish; the final product may not be perfect, but it’s still edible. Similarly, in weakly supervised learning, the model is trained using coarse annotations that provide only basic instructions, resulting in an imperfect segmentation outcome. However, this approach allows for faster and more efficient training, enabling the creation of better models overall.
The article then delves into the specifics of weakly supervised instance segmentation, including the various types of annotations used (image-level labels, points, scribble, boxes) and the different approaches proposed by recent studies to tackle this task (e.g., RWSeg, ScribbleSeg). The authors also mention challenges like class imbalance and noisy annotations, which can affect the model’s performance.
Finally, the authors highlight some of the state-of-the-art methods for weakly supervised instance segmentation, such as Fully Sparse 3D Object Detection (Fan et al., 2022) and Cross-graph Competing Random Walks (Dong et al., 2022). These models have demonstrated impressive performance on benchmark datasets, showcasing the potential of weakly supervised learning in this area.
In summary, the article provides an insightful overview of recent works on weakly supervised instance segmentation, demonstrating how this approach can help overcome the challenges of manual annotation and accelerate the development of high-performance computer vision models.