Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
Authors: Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 Poly Diffuse significantly advances the current state of the art and enables broader practical applications. |
| Researcher Affiliation | Academia | Jiacheng Chen Ruizhi Deng Yasutaka Furukawa Simon Fraser University |
| Pseudocode | Yes | Algorithm 1 Guidance training (stage 1) ... Algorithm 2 Denoising training (stage 2) |
| Open Source Code | Yes | The code and data are available on our project page: https://poly-diffuse.github.io. |
| Open Datasets | Yes | Structured3D dataset [48] contains 3500 indoor scenes (3000/250/250 for training/validation/test) with diverse house floorplans. ... The nu Scenes dataset [2] provides a standard benchmark for HD map reconstruction. |
| Dataset Splits | Yes | Structured3D dataset [48] contains 3500 indoor scenes (3000/250/250 for training/validation/test) with diverse house floorplans. |
| Hardware Specification | Yes | We have implemented the system with Py Torch and used a machine with 4 NVIDIA RTX A5000 GPUs. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number. It also mentions borrowing the codebase of "Karras et al.[19]" without specific versions for that framework or other libraries. |
| Experiment Setup | Yes | The loss weights for the guidance training are λ1 = 1, λ2 = 0.05, λ3 = 0.1. ... Adam optimizer is employed with a learning rate of 2e-4 and a weight decay rate of 1e-4. ... We employ an Adam optimizer with a base learning rate of 6e-4 and a weight decay factor of 1e-4. A cosine learning rate scheduler is used. |