TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Authors: Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan, Changil Kim, Christian Richardt, James Tompkin, Matthew O'Toole
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, Table 2: Phasor supervision aids few-view reconstruction. Each cell contains RGB image similarity measures, and metrics are computed on 10 hold-out views. |
| Researcher Affiliation | Collaboration | Benjamin Attal Carnegie Mellon University Eliot Laidlaw Brown University Aaron Gokaslan Cornell University Changil Kim Facebook Christian Richardt University of Bath James Tompkin Brown University Matthew O Toole Carnegie Mellon University |
| Pseudocode | No | The paper describes methods through text and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper links to a project website (imaging.cs.cmu.edu/torf) and mentions videos, but does not explicitly state that the source code for the methodology described is available there or elsewhere. |
| Open Datasets | No | The paper states, 'We captured the Phone Booth, Cup, Photocopier, Desk Box, and Study Book sequences with our handheld camera setup.' and 'Finally, we create synthetic raw C-To F sequences Bathroom, Bedroom, and Dino Pear by adapting the physically-based path tracer PBRT [40] to generate phasor images with multi-bounce and scattering effects.' These are custom datasets, and no access information (link, DOI, or explicit public availability) is provided. |
| Dataset Splits | No | The paper refers to 'hold-out views' used for evaluation, which serve as test sets, but it does not explicitly describe a distinct validation set or its specific split details for hyperparameter tuning during training. |
| Hardware Specification | Yes | For optimization, we use an NVIDIA Ge Force RTX 2080 Ti with 11 GB RAM. |
| Software Dependencies | No | The paper mentions 'We use Open CV to calibrate intrinsics, extrinsics, and lens distortion.' but does not specify a version number for OpenCV or any other software dependencies. |
| Experiment Setup | Yes | First, we optimize the weights of the static neural network F stat θ , as well as the camera poses for each video frame and the relative rotation and translation between the color and C-To F sensor, with a learning rate of 10 3. After 5000 iterations, we decrease the pose learning rate to 5 10 4, and optimize our full model. At training time, we reduce the weight λ in later iterations to prioritize the color loss (halved every 125,000 iterations). |