RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency
Authors: Zhuoman Liu, Bo Yang, Yan Luximon, Ajay Kumar, Jinxi Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our method on three public datasets, demonstrating remarkable performance in 3D surface point reconstruction on both synthetic and challenging real-world 3D scenes, clearly surpassing existing coordinate-based and ray-based baselines. Most notably, our method achieves a 1000 faster speed than coordinate-based methods to render an 800 800 depth image, showing the superiority of our method for 3D shape representation. |
| Researcher Affiliation | Academia | Zhuoman Liu Bo Yang Yan Luximon Ajay Kumar Jinxi Li v LAR Group, The Hong Kong Polytechnic University {zhuo-man.liu, jinxi.li}@connect.polyu.hk bo.yang@polyu.edu.hk |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code and data are available at https://github.com/v LAR-group/Ray DF |
| Open Datasets | Yes | Our method is evaluated on three types of public datasets: 1) the object-level synthetic Blender dataset from the original Ne RF paper [47], 2) the scene-level synthetic DM-SR dataset from the recent DM-Ne RF paper [73], and 3) the scene-level real-world Scan Net dataset [16]. |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., '100 views for training and 200 novel views for testing' for Blender dataset) but does not explicitly mention a separate validation split. |
| Hardware Specification | Yes | we conduct a simple experiment to generate an 800 800 depth image on a computer with a single NVIDIA RTX 3090 GPU card and a CPU of AMD Ryzen 7. |
| Software Dependencies | No | The paper mentions using Adam optimizer and SIREN network architecture but does not specify version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | We use a 13-layer SIREN [63] with 1024 hidden units to learn a ray-surface distance field. We set the batch size as 8192 and train the network for 10 epochs with the Adam [35] optimizer and a cosine annealing strategy [44] with the learning rate initialized as 10 5 and decayed to 10 8. For the auxiliary network, we use a 7-layer SIREN with 512 hidden units, with batch sizes of 2048 for Blender and ScanNet, and 1024 for DM-SR, training for 5 epochs with Adam and a cycle annealing strategy with max learning rate 10 4. |