PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models

• URL: http://arxiv.org/abs/2306.01461v2
• Date: Mon, 25 Dec 2023 03:55:07 GMT
• Title: PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
• Authors: Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa
• Abstract summary: PolyDiffuse is a novel structured reconstruction algorithm that transforms visual sensor data into polygonal shapes with Diffusion Models (DM) DM is an emerging machinery amid exploding generative AI, while formulating reconstruction as a generation process conditioned on sensor data. We have evaluated our approach for reconstructing two types of polygonal shapes: floorplan as a set of polygons and HD map for autonomous cars as a set of polylines.
• Score: 26.819929072916363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents PolyDiffuse, a novel structured reconstruction algorithm that transforms visual sensor data into polygonal shapes with Diffusion Models (DM), an emerging machinery amid exploding generative AI, while formulating reconstruction as a generation process conditioned on sensor data. The task of structured reconstruction poses two fundamental challenges to DM: 1) A structured geometry is a ``set'' (e.g., a set of polygons for a floorplan geometry), where a sample of $N$ elements has $N!$ different but equivalent representations, making the denoising highly ambiguous; and 2) A ``reconstruction'' task has a single solution, where an initial noise needs to be chosen carefully, while any initial noise works for a generation task. Our technical contribution is the introduction of a Guided Set Diffusion Model where 1) the forward diffusion process learns guidance networks to control noise injection so that one representation of a sample remains distinct from its other permutation variants, thus resolving denoising ambiguity; and 2) the reverse denoising process reconstructs polygonal shapes, initialized and directed by the guidance networks, as a conditional generation process subject to the sensor data. We have evaluated our approach for reconstructing two types of polygonal shapes: floorplan as a set of polygons and HD map for autonomous cars as a set of polylines. Through extensive experiments on standard benchmarks, we demonstrate that PolyDiffuse significantly advances the current state of the art and enables broader practical applications.

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