SLiMe: Segment Like Me

Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence, and 3D shape generation. Inspired by these advancements, we explore leveraging these extensive vision-language models for segmenting images at any desired granularity using as few as one annotated sample by proposing SLiMe. SLiMe frames this problem as an optimization task. Specifically, given a single training image and its segmentation mask, we first extract attention maps, including our novel "weighted accumulated self-attention map" from the SD prior. Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image. These learned embeddings then highlight the segmented region in the attention maps, which in turn can then be used to derive the segmentation map. This enables SLiMe to segment any real-world image during inference with the granularity of the segmented region in the training image, using just one example. Moreover, leveraging additional training data when available, i.e. few-shot, improves the performance of SLiMe. We carried out a knowledge-rich set of experiments examining various design factors and showed that SLiMe outperforms other existing one-shot and few-shot segmentation methods.

DualCSG: Learning Dual CSG Trees for General and Compact CAD Modeling

We present DualCSG, a novel neural network composed of two dual and complementary branches for unsupervised learning of constructive solid geometry (CSG) representations of 3D CAD shapes. Our network is trained to reconstruct a given 3D CAD shape through a compact assembly of quadric surface primitives via fixed-order CSG operations along two branches. The key difference between our method and all previous neural CSG models is that DualCSG has a dedicated branch, the residual branch, to assemble the potentially complex, complement or residual shape that is to be subtracted from an overall cover shape. The cover shape is modeled by the other branch, the cover branch. Both branches construct a union of primitive intersections, where the only difference is that the residual branch also learns primitive inverses while operating in the complement space. With the shape complements, our network is provably general. We demonstrate both quantitatively and qualitatively that our network produces CSG reconstructions with superior quality, more natural trees, and better quality-compactness tradeoff than all existing alternatives, especially over complex and high-genus CAD shapes.