Non-Rigid Shape Registration via Deep Functional Maps Prior
Authors: Puhua Jiang, Mingze Sun, Ruqi Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that, with as few as dozens of training shapes of limited variability, our pipeline achieves state-of-the-art results on several benchmarks of non-rigid point cloud matching, but also delivers high-quality correspondences between unseen challenging shape pairs that undergo both significant extrinsic and intrinsic deformations, in which case neither traditional registration methods nor intrinsic methods work. |
| Researcher Affiliation | Academia | Puhua Jiang1,2# Mingze Sun1# Ruqi Huang1 1. Tsinghua Shenzhen International Graduate School, China 2. Peng Cheng Lab, China |
| Pseudocode | Yes | Algorithm 1: Shape registration pipeline. Input: Source mesh S = {V, E} and target point cloud T ;Trained point feature extractor F Output: X converging to a local minimum of Etotal; Deformed source model {V , E}; Correspondence Π ST , Π T S between S and T . |
| Open Source Code | Yes | The code is available at https://github.com/rqhuang88/DFR. |
| Open Datasets | Yes | Datasets: We evaluate our method and several state-of-the-art techniques for estimating correspondences between deformable shapes on an array of benchmarks as follows: FAUST_r: The remeshed version of FAUST dataset [4], which consists of 100 human shapes (10 individuals performing the same 10 actions). We split the first 80 as training shapes and the rest as testing shapes; SCAPE_r: The remeshed version of SCAPE dataset [2], which consists 71 human shapes (same individual in varying poses). We split the first 51 as training shapes and the rest as testing shapes; SHREC19_r: The remehsed version of SHREC19 dataset [36]... |
| Dataset Splits | No | The paper describes training and testing splits, for example, 'We split the first 80 as training shapes and the rest as testing shapes' for FAUST_r. However, it does not explicitly specify a separate validation split or its size for hyperparameter tuning. |
| Hardware Specification | No | The paper does not explicitly state the specific hardware used for its experiments, such as GPU or CPU models. It mentions a V100 GPU in the context of a baseline's memory limitations, but not for their own experimental setup. |
| Software Dependencies | No | The paper states 'We implement our framework in Py Torch' and 'We use modified DGCNN [24] the backbone of our feature extractor.' However, specific version numbers for these software dependencies are not provided. |
| Experiment Setup | Yes | For training the DFM network, in Eqn.(6) of the main text, we empirically set λbij = 1.0, λorth = 1.0, λalign = 1e-4, λNCE = 1.0. We train our feature extractor with the Adam optimizer with a learning rate equal to 2e-3. The batch size is chosen to be 4 for all datasets. Regarding the registration optimization, in Eqn.(12) of the main text, we empirically set λcd = 0.01, λcorr = 1.0, λarap = 20 in Stage-I and λcd = 1.0, λcorr = 0.01, λarap = 1 in Stage-II. For α in Eqn.(9) of the main text, we set α = 0.2. |