Learning to Infer Implicit Surfaces without 3D Supervision
Authors: Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superiority of our method over state-of-the-art unsupervised 3D deep learning techniques, that are based on alternative shape representations, in terms of quantitative and qualitative measures. Comprehensive ablation studies also verify the efficacy of proposed probing-based sampling technique and the implicit geometric regularization. |
| Researcher Affiliation | Collaboration | Shichen Liu , , Shunsuke Saito , , Weikai Chen (B) , and Hao Li , , USC Institute for Creative Technologies University of Southern California Pinscreen {liushichen95, shunsuke.saito16, chenwk891}@gmail.com hao@hao-li.com |
| Pseudocode | No | The information is insufficient. The paper describes its methods but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The information is insufficient. The paper does not contain any explicit statement about providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate our method on Shape Net [10] dataset. We focus on 6 commonly used categories with complex topologies: plane, bench, table, car, chair and boat. We use the same train/validate/test split as in [4, 1, 2] and the rendered images (64 64 resolution) provided by [1] which consist of 24 views for each object. |
| Dataset Splits | Yes | We use the same train/validate/test split as in [4, 1, 2] and the rendered images (64 64 resolution) provided by [1] which consist of 24 views for each object. |
| Hardware Specification | Yes | We train the network using Adam optimizer with learning rate of 1 10 4 and batch size of 8 on a single 1080Ti GPU. |
| Software Dependencies | No | The information is insufficient. The paper mentions using a pre-trained ResNet18 and Adam optimizer but does not specify version numbers for any software dependencies like deep learning frameworks, Python, or CUDA. |
| Experiment Setup | Yes | Implementation details. We adopt a pre-trained Res Net18 as the encoder, which outputs a latent code of 128 dimensions. The decoder is realized using 6 fully-connected layers (output channels as 2048, 1024, 512, 256, 128 and 1 respectively) followed by a sigmoid activation function. We sample Np = 16, 000 anchor points in 3D space and Nr = 4096 rays for each view. The sampling bandwidth σ is set as 7 10 3. The radius τ of the supporting region is set as 3 10 2. For the regularizer, we set d = 3 10 2, λ = 1 10 2, and norm p = 0.8. We train the network using Adam optimizer with learning rate of 1 10 4 and batch size of 8 on a single 1080Ti GPU. |