Robust Camera Pose Refinement for Multi-Resolution Hash Encoding
Authors: Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J. Kim, Jin-Hwa Kim
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the novel-view synthesis datasets validate that our learning frameworks achieve state-of-the-art performance and rapid convergence of neural rendering. In this section, we validate our proposed method using the multi-resolution hash encoding (M uller et al., 2022) with inaccurate or unknown camera poses. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Korea University, Republic of Korea 2NAVER AI Lab, Republic of Korea 3AI Institute of Seoul National University, Republic of Korea. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions re-implementing a training pipeline but does not provide an explicit statement about releasing their source code or a link to a repository. |
| Open Datasets | Yes | We evaluate the proposed method against the two previous works, BARF (Lin et al., 2021) and GARF (Chng et al., 2022)... Ne RF-Synthetic (Mildenhall et al., 2020) has 8 synthetic object-centric scenes, which consist of 100 rendered images with ground-truth camera poses (intrinsic and extrinsic) for each scene... LLFF (Mildenhall et al., 2019) has 8 forwardfacing scenes captured by a hand-held camera, including RGB images and camera poses that have been estimated using the off-the-shelf algorithm (Sch onberger & Frahm, 2016). |
| Dataset Splits | No | The paper uses standard datasets (Ne RF-Synthetic and LLFF) for novel-view synthesis and evaluation, but it does not explicitly provide the specific training/validation/test splits (e.g., percentages or sample counts) used for its experiments. |
| Hardware Specification | No | The paper mentions using a 'tiny-cuda-nn' framework and the 'NAVER Smart Machine Learning (NSML) platform', but does not specify exact hardware components like GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using 'Py Torch' and 'tiny-cuda-nn (tcnn)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For the multi-resolution hash encoding, we follow the approach of Instant-NGP (M uller et al., 2022), which uses a table size of T = 219 and a dimensionality of F = 2 for each level feature. Each feature table is initialized with a uniform distribution U[ 1e-4, 1e-4]... The decoding network consists of 6-layer MLPs with Re LU (Glorot et al., 2011) activation and 256 hidden dimensions... We use the Adam optimizer and train all models for 200K iterations, with a learning rate of 5 × 10−4 that exponentially decays to 1 × 10−4. For multi-level learning rate scheduling, we set the scheduling interval [ts, te] = [20K, 100K]... While we set λ = 1 by default for the straight-through estimator... |