Polygonizer: An auto-regressive building delineator
In geospatial planning, it is often essential to represent objects in a vectorized format, as this format easily translates to downstream tasks such as web development, graphics, or design. While these problems are frequently addressed using semantic segmentation, which requires additional post-processing to vectorize objects in a non-trivial way, we present an Image-to-Sequence model that allows for direct shape inference and is ready for vector-based workflows out of the box. We demonstrate the model's performance in various ways, including perturbations to the image input that correspond to variations or artifacts commonly encountered in remote sensing applications. Our model outperforms prior works when using ground truth bounding boxes (one object per image), achieving the lowest maximum tangent angle error.
Code (0)
등록된 구현이 없습니다.
Tasks
Semantic SegmentationSimilar Papers 제목 키워드 기반
Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing
This paper addresses the challenge of mapping polygonal buildings from remote sensing images and introduces a novel algorithm, the Global Collinearity-aware Polygonizer (GCP). GCP, built upon an instance segmentation fra…
Instance SegmentationSemantic SegmentationBuildAnyPoint: 3D Building Structured Abstraction from Diverse Point Clouds
We introduce BuildAnyPoint, a novel generative framework for structured 3D building reconstruction from point clouds with diverse distributions, such as those captured by airborne LiDAR and Structure-from-Motion. To reco…
Point Cloud CompletionPoint CloudsTranslating Images to Road Network: A Non-Autoregressive Sequence-to-Sequence Approach
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections. However, generating road network poses a …
SnowyLane: Robust Lane Detection on Snow-covered Rural Roads Using Infrastructural Elements
Lane detection for autonomous driving in snow-covered environments remains a major challenge due to the frequent absence or occlusion of lane markings. In this paper, we present a novel, robust and realtime capable appro…
Autonomous DrivingLane DetectionInfinityStar: Unified Spacetime AutoRegressive Modeling for Visual Generation
We introduce InfinityStar, a unified spacetime autoregressive framework for high-resolution image and dynamic video synthesis. Building on the recent success of autoregressive modeling in both vision and language, our pu…
Video Generation