PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
Authors: Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |