DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field
Authors: Chenyangguang Zhang, Yan Di, Ruida Zhang, Guangyao Zhai, Fabian Manhardt, Federico Tombari, Xiangyang Ji
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and real-world datasets demonstrate that DDF-HO consistently outperforms all baseline methods by a large margin, especially under Chamfer Distance, with about 80% leap forward. Codes are available at https://github.com/ZhangCYG/DDFHO. |
| Researcher Affiliation | Collaboration | Chenyangguang Zhang1 , Yan Di2 , Ruida Zhang1 , Guangyao Zhai2, Fabian Manhardt3, Federico Tombari2,3, Xiangyang Ji1 1Tsinghua University, 2Technical University of Munich, 3 Google |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/ZhangCYG/DDFHO. |
| Open Datasets | Yes | A synthetic dataset Ob Man [29] and two real-world datasets HO3D(v2) [28], MOW [6] are utilized to evaluate DDF-HO in various scenarios. Ob Man consists of 2772 objects of 8 categories from Shape Net [8], with 21K grasps generated by Grasp It [43]. HO3D(v2) [28] contains 77,558 images from 68 sequences with 10 different persons manipulating 10 different YCB objects [5]. MOW [6] comprises a total of 442 images and 121 object templates, collected from in-the-wild hand-object interaction datasets [14, 56]. |
| Dataset Splits | Yes | We follow [29, 67] to split the training and testing sets. HO3D(v2) [28]... We follow [28] to split training and testing sets. MOW [6]... The training and testing splits remain the same as the released code of [67]. |
| Hardware Specification | Yes | We conduct the training, evaluation and visualization of DDF-HO on a single A100 40GB GPU. |
| Software Dependencies | No | The paper mentions 'Trimesh 2' but does not provide specific version numbers for other key software components, libraries, or programming languages used (e.g., Python, PyTorch, CUDA) to reproduce the experiment. |
| Experiment Setup | Yes | The number of sampled points Kl along the projected 2D ray is set to 8 and number of multi-head attention is 2 for 2D Ray-Based Feature Aggregation technique. K3D for FL 3D introduced in Sec. 3.4 is set as 8. DDF-HO is trained end-to-end using Adam with a learning rate of 1e-4 on Ob Man for 100 epochs. Following [67], we use the network weights learned on synthetic Ob Man to initialize the training on HO3D(v2) and MOW. Training on HO3D(v2) and MOW also use Adam optimizer with a learning rate 1e-5 for another 100 and 10 epochs, respectively following [67]. The weighting factors of the loss for DDF-HO λ1, λ2 are set to 5.0 and 0.5, respectively. |