Learning 3D Equivariant Implicit Function with Patch-Level Pose-Invariant Representation
Authors: Xin Hu, Xiaole Tang, Ruixuan Yu, Jian Sun
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our model achieves state-of-the-art performance on multiple surface reconstruction datasets, and also exhibits better generalization to crossdataset shapes and robustness to arbitrary rotations. |
| Researcher Affiliation | Collaboration | Xin Hu1, Xiaole Tang1, Ruixuan Yu2, Jian Sun(B)1,3 1 Xi an Jiaotong University, Xi an, China 2 Shandong University, Weihai, China 3 Pazhou Laboratory (Huangpu), Guangzhou, China |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code will be available at https://github.com/math Xin112/PEIF.git. |
| Open Datasets | Yes | Datasets. We experiment on four datasets including Shape Net [51], ABC [52], Synthetic Rooms [19], MGN [53]. (1) Shape Net [51], as pre-processed by [7], contains watertight meshes of shapes in 13 classes. ... (2) ABC [52] has one million CAD models, mainly mechanical objects. We use the splits from [54] and select watertight meshes for experiments: 3599/883/98 shapes for training/validation/testing. (3) Synthetic Rooms [19] contains 5k synthetic room scenes composed of random walls, floors, and Shape Net objects. We adopt the same train/validation/test division in [19]. (4) MGN [53] is a real scanned dataset containing 5 clothing categories. |
| Dataset Splits | Yes | We use the splits from [54] and select watertight meshes for experiments: 3599/883/98 shapes for training/validation/testing. |
| Hardware Specification | Yes | We conducted all experiments on one NVIDIA RTX 4090 GPU. |
| Software Dependencies | No | We implement our PEIF in Pytorch [49] using Adam optimizer [50]. No specific version numbers for PyTorch or Adam are provided. |
| Experiment Setup | Yes | The learning rate is 8 × 10−4. For each query point, the size of the neighborhood is set as K = 32 for Shape Net [51] and ABC [52] datasets, K = 54 for Synthetic Rooms [19] dataset. We set β = 0.1 in the training loss and Nm = 4 for the memory bank. Please refer to the Appendix for details on the structures of involved MLPs, and the effect of different values of β. ... MLPs. γθs in Eqn. ( 7) consists of four 1 × 1 convolution layers with 6, 32, 64, and 128 hidden units. γθq and γθq in Eqn. (8) consists of 1 × 1 convolution layers with 3, 32, 64, and 128 hidden units while these for γθp are 3, 32, 64, and 128. For γθa in Eqn. ( 14), the unit numbers are 256, 512, 256, and 384. For γθd, the unit numbers are 256, 256, 256, and 256. All feature dimensions are 128. For the multi-head memory bank M, the number of the memory bank is set as NM = 4, with each memory bank containing 596 items. |