Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Authors: Wenzheng Chen, Huan Ling, Jun Gao, Edward Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively using 2D supervision. Our project website is: https://nv-tlabs.github.io/DIB-R/. We demonstrate the effectiveness of our framework through three challenging ML applications. across which we achieve both numerical and visual state-of-the art results. |
| Researcher Affiliation | Collaboration | NVIDIA1 University of Toronto2 Vector Institute3 Mc Gill University4 Aalto University5 {wenzchen, huling, jung, esmith, jlehtinen, sfidler}@nvidia.com, jacobson@cs.toronto.edu |
| Pseudocode | No | No explicit pseudocode or algorithm block was found. The method is described mathematically through equations (1, 2, 4, 5, 6) and in prose. |
| Open Source Code | Yes | Our project website is: https://nv-tlabs.github.io/DIB-R/ |
| Open Datasets | Yes | Dataset: As in [14, 20, 33], our dataset comprises 13 object categories from the Shape Net dataset [3]. Following CMR [13], we adopt CUB bird dataset [35] and PASCAL3D+ car dataset [38]. |
| Dataset Splits | Yes | We use the same split of objects into our training and test set as [33]. |
| Hardware Specification | No | No specific hardware details (e.g., GPU model, CPU type, memory) are mentioned for the experimental setup. The paper does not specify the computing environment used for training or inference. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, TensorFlow 2.x) are explicitly listed in the paper. |
| Experiment Setup | Yes | In our experiments, we set λcol = 1, λsm = 0.001, and λlap = 0.01. The network is optimized using the Adam optimizer [15], with α = 0.0001, β1 = 0.9, and β2 = 0.999. The batch size is 64, and the dimension of input image is 64 64. We set λadv = 0.5, λgp = 0.5, and λper = 1. We fix the learning rate for the discriminator to 1e 5 and optimize using Adam [15], with α = 0.0001, β1 = 0.5, and β2 = 0.999. |