README.md

@@ -459,6 +459,482 @@ We provide two visualization tools for debugging.
 
 
 
+#### Speed and quality tradeoff
+We have reported some ablation experiments in our paper supplementary material.
+Setting `N_iters`, `N_rand`, `num_voxels`, `rgbnet_depth`, `rgbnet_width` to larger values or setting `stepsize` to smaller values typically leads to better quality but need more computation.
+The `weight_distortion` affects the training speed and quality as well.
+Only `stepsize` is tunable in testing phase, while all the other fields should remain the same as training.
+
+## Advanced data structure
+- **Octree** — [Plenoxels: Radiance Fields without Neural Networks](https://alexyu.net/plenoxels/).
+- **Hash** — [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://nvlabs.github.io/instant-ngp/).
+- **Factorized components** — [TensoRF: Tensorial Radiance Fields](https://apchenstu.github.io/TensoRF/).
+
+You will need them for scaling to a higher grid resolution. But we believe our simplest dense grid could still be your good starting point if you have other challenging problems to deal with.
+
+## Acknowledgement
+The code base is origined from an awesome [nerf-pytorch](https://github.com/yenchenlin/nerf-pytorch) implementation, but it becomes very different from the code base now.
+# DirectVoxGO
+
+Direct Voxel Grid Optimization (CVPR2022 Oral, [project page](https://sunset1995.github.io/dvgo/), [DVGO paper](https://arxiv.org/abs/2111.11215), [DVGO v2 paper](https://arxiv.org/abs/2206.05085)).
+
+https://user-images.githubusercontent.com/2712505/153380311-19d6c3a1-9130-489a-af16-ad36c78f10a9.mp4
+
+https://user-images.githubusercontent.com/2712505/153380197-991d1689-6418-499c-a192-d757f9a64b64.mp4
+
+### Custom casual capturing
+A [short guide](https://sunset1995.github.io/dvgo/tutor_forward_facing.html) to capture custom forward-facing scenes and rendering fly-through videos.
+
+Below are two rgb and depth fly-through videos from custom captured scenes.
+
+https://user-images.githubusercontent.com/2712505/174267754-619d4f81-dd04-4c50-ba7f-434774cb890e.mp4
+
+### Features
+- Speedup NeRF by replacing the MLP with the voxel grid.
+- Simple scene representation:
+    - *Volume densities*: dense voxel grid (3D).
+    - *View-dependent colors*: dense feature grid (4D) + shallow MLP.
+- Pytorch cuda extention built just-in-time for another 2--3x speedup.
+- O(N) realization for the distortion loss proposed by [mip-nerf 360](https://jonbarron.info/mipnerf360/).
+    - The loss improves our training time and quality.
+    - We have released a self-contained pytorch package: [torch_efficient_distloss](https://github.com/sunset1995/torch_efficient_distloss).
+    - Consider a batch of 8192 rays X 256 points.
+        - GPU memory consumption: 6192MB => 96MB.
+        - Run times for 100 iters: 20 sec => 0.2sec.
+- Supported datasets:
+    - *Bounded inward-facing*: [NeRF](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1), [NSVF](https://dl.fbaipublicfiles.com/nsvf/dataset/Synthetic_NSVF.zip), [BlendedMVS](https://dl.fbaipublicfiles.com/nsvf/dataset/BlendedMVS.zip), [T&T (masked)](https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip), [DeepVoxels](https://drive.google.com/open?id=1ScsRlnzy9Bd_n-xw83SP-0t548v63mPH).
+    - *Unbounded inward-facing*: [T&T](https://drive.google.com/file/d/11KRfN91W1AxAW6lOFs4EeYDbeoQZCi87/view?usp=sharing), [LF](https://drive.google.com/file/d/1gsjDjkbTh4GAR9fFqlIDZ__qR9NYTURQ/view?usp=sharing), [mip-NeRF360](https://jonbarron.info/mipnerf360/).
+    - *Foward-facing*: [LLFF](https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7).
+
+
+### Installation
+```
+git clone git@github.com:sunset1995/DirectVoxGO.git
+cd DirectVoxGO
+pip install -r requirements.txt
+```
+[Pytorch](https://pytorch.org/) and [torch_scatter](https://github.com/rusty1s/pytorch_scatter) installation is machine dependent, please install the correct version for your machine.
+
+<details>
+  <summary> Dependencies (click to expand) </summary>
+
+  - `PyTorch`, `numpy`, `torch_scatter`: main computation.
+  - `scipy`, `lpips`: SSIM and LPIPS evaluation.
+  - `tqdm`: progress bar.
+  - `mmcv`: config system.
+  - `opencv-python`: image processing.
+  - `imageio`, `imageio-ffmpeg`: images and videos I/O.
+  - `Ninja`: to build the newly implemented torch extention just-in-time.
+  - `einops`: torch tensor shaping with pretty api.
+  - `torch_efficient_distloss`: O(N) realization for the distortion loss.
+</details>
+
+
+## Directory structure for the datasets
+
+<details>
+  <summary> (click to expand;) </summary>
+
+    data
+    ├── nerf_synthetic     # Link: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
+    │   └── [chair|drums|ficus|hotdog|lego|materials|mic|ship]
+    │       ├── [train|val|test]
+    │       │   └── r_*.png
+    │       └── transforms_[train|val|test].json
+    │
+    ├── Synthetic_NSVF     # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/Synthetic_NSVF.zip
+    │   └── [Bike|Lifestyle|Palace|Robot|Spaceship|Steamtrain|Toad|Wineholder]
+    │       ├── intrinsics.txt
+    │       ├── rgb
+    │       │   └── [0_train|1_val|2_test]_*.png
+    │       └── pose
+    │           └── [0_train|1_val|2_test]_*.txt
+    │
+    ├── BlendedMVS         # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/BlendedMVS.zip
+    │   └── [Character|Fountain|Jade|Statues]
+    │       ├── intrinsics.txt
+    │       ├── rgb
+    │       │   └── [0|1|2]_*.png
+    │       └── pose
+    │           └── [0|1|2]_*.txt
+    │
+    ├── TanksAndTemple     # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip
+    │   └── [Barn|Caterpillar|Family|Ignatius|Truck]
+    │       ├── intrinsics.txt
+    │       ├── rgb
+    │       │   └── [0|1|2]_*.png
+    │       └── pose
+    │           └── [0|1|2]_*.txt
+    │
+    ├── deepvoxels         # Link: https://drive.google.com/drive/folders/1ScsRlnzy9Bd_n-xw83SP-0t548v63mPH
+    │   └── [train|validation|test]
+    │       └── [armchair|cube|greek|vase]
+    │           ├── intrinsics.txt
+    │           ├── rgb/*.png
+    │           └── pose/*.txt
+    │
+    ├── nerf_llff_data     # Link: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
+    │   └── [fern|flower|fortress|horns|leaves|orchids|room|trex]
+    │
+    ├── tanks_and_temples  # Link: https://drive.google.com/file/d/11KRfN91W1AxAW6lOFs4EeYDbeoQZCi87/view?usp=sharing
+    │   └── [tat_intermediate_M60|tat_intermediate_Playground|tat_intermediate_Train|tat_training_Truck]
+    │       └── [train|test]
+    │           ├── intrinsics/*txt
+    │           ├── pose/*txt
+    │           └── rgb/*jpg
+    │
+    ├── lf_data            # Link: https://drive.google.com/file/d/1gsjDjkbTh4GAR9fFqlIDZ__qR9NYTURQ/view?usp=sharing
+    │   └── [africa|basket|ship|statue|torch]
+    │       └── [train|test]
+    │           ├── intrinsics/*txt
+    │           ├── pose/*txt
+    │           └── rgb/*jpg
+    │
+    ├── 360_v2             # Link: https://jonbarron.info/mipnerf360/
+    │   └── [bicycle|bonsai|counter|garden|kitchen|room|stump]
+    │       ├── poses_bounds.npy
+    │       └── [images_2|images_4]
+    │
+    ├── nerf_llff_data     # Link: https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7
+    │   └── [fern|flower|fortress|horns|leaves|orchids|room|trex]
+    │       ├── poses_bounds.npy
+    │       └── [images_2|images_4]
+    │
+    └── co3d               # Link: https://github.com/facebookresearch/co3d
+        └── [donut|teddybear|umbrella|...]
+            ├── frame_annotations.jgz
+            ├── set_lists.json
+            └── [129_14950_29917|189_20376_35616|...]
+                ├── images
+                │   └── frame*.jpg
+                └── masks
+                    └── frame*.png
+</details>
+
+
+
+## GO
+
+- Training
+    ```bash
+    $ python run.py --config configs/nerf/lego.py --render_test
+    ```
+    Use `--i_print` and `--i_weights` to change the log interval.
+- Evaluation
+    To only evaluate the testset `PSNR`, `SSIM`, and `LPIPS` of the trained `lego` without re-training, run:
+    ```bash
+    $ python run.py --config configs/nerf/lego.py --render_only --render_test \
+                                                  --eval_ssim --eval_lpips_vgg
+    ```
+    Use `--eval_lpips_alex` to evaluate LPIPS with pre-trained Alex net instead of VGG net.
+- Render video
+    ```bash
+    $ python run.py --config configs/nerf/lego.py --render_only --render_video
+    ```
+    Use `--render_video_factor 4` for a fast preview.
+- Reproduction: all config files to reproduce our results.
+    <details>
+        <summary> (click to expand) </summary>
+
+        $ ls configs/*
+        configs/blendedmvs:
+        Character.py  Fountain.py  Jade.py  Statues.py
+
+        configs/nerf:
+        chair.py  drums.py  ficus.py  hotdog.py  lego.py  materials.py  mic.py  ship.py
+
+        configs/nsvf:
+        Bike.py  Lifestyle.py  Palace.py  Robot.py  Spaceship.py  Steamtrain.py  Toad.py  Wineholder.py
+
+        configs/tankstemple:
+        Barn.py  Caterpillar.py  Family.py  Ignatius.py  Truck.py
+
+        configs/deepvoxels:
+        armchair.py  cube.py  greek.py  vase.py
+
+        configs/tankstemple_unbounded:
+        M60.py  Playground.py  Train.py  Truck.py
+
+        configs/lf:
+        africa.py  basket.py  ship.py  statue.py  torch.py
+
+        configs/nerf_unbounded:
+        bicycle.py  bonsai.py  counter.py  garden.py  kitchen.py  room.py  stump.py
+
+        configs/llff:
+        fern.py  flower.py  fortress.py  horns.py  leaves.py  orchids.py  room.py  trex.py
+    </details>
+
+### Custom casually captured scenes
+Coming soon hopefully.
+
+### Development and tuning guide
+#### Extention to new dataset
+Adjusting the data related config fields to fit your camera coordinate system is recommend before implementing a new one.
+We provide two visualization tools for debugging.
+1. Inspect the camera and the allocated BBox.
+    - Export via `--export_bbox_and_cams_only {filename}.npz`:
+      ```bash
+      python run.py --config configs/nerf/mic.py --export_bbox_and_cams_only cam_mic.npz
+      ```
+    - Visualize the result:
+      ```bash
+      python tools/vis_train.py cam_mic.npz
+      ```
+2. Inspect the learned geometry after coarse optimization.
+    - Export via `--export_coarse_only {filename}.npz` (assumed `coarse_last.tar` available in the train log):
+      ```bash
+      python run.py --config configs/nerf/mic.py --export_coarse_only coarse_mic.npz
+      ```
+    - Visualize the result:
+      ```bash
+      python tools/vis_volume.py coarse_mic.npz 0.001 --cam cam_mic.npz
+      ```
+
+| Inspecting the cameras & BBox | Inspecting the learned coarse volume |
+|:-:|:-:|
+|![](figs/debug_cam_and_bbox.png)|![](figs/debug_coarse_volume.png)|
+
+
+
+#### Speed and quality tradeoff
+We have reported some ablation experiments in our paper supplementary material.
+Setting `N_iters`, `N_rand`, `num_voxels`, `rgbnet_depth`, `rgbnet_width` to larger values or setting `stepsize` to smaller values typically leads to better quality but need more computation.
+The `weight_distortion` affects the training speed and quality as well.
+Only `stepsize` is tunable in testing phase, while all the other fields should remain the same as training.
+
+## Advanced data structure
+- **Octree** — [Plenoxels: Radiance Fields without Neural Networks](https://alexyu.net/plenoxels/).
+- **Hash** — [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://nvlabs.github.io/instant-ngp/).
+- **Factorized components** — [TensoRF: Tensorial Radiance Fields](https://apchenstu.github.io/TensoRF/).
+
+You will need them for scaling to a higher grid resolution. But we believe our simplest dense grid could still be your good starting point if you have other challenging problems to deal with.
+
+## Acknowledgement
+The code base is origined from an awesome [nerf-pytorch](https://github.com/yenchenlin/nerf-pytorch) implementation, but it becomes very different from the code base now.
+# DirectVoxGO
+
+Direct Voxel Grid Optimization (CVPR2022 Oral, [project page](https://sunset1995.github.io/dvgo/), [DVGO paper](https://arxiv.org/abs/2111.11215), [DVGO v2 paper](https://arxiv.org/abs/2206.05085)).
+
+https://user-images.githubusercontent.com/2712505/153380311-19d6c3a1-9130-489a-af16-ad36c78f10a9.mp4
+
+https://user-images.githubusercontent.com/2712505/153380197-991d1689-6418-499c-a192-d757f9a64b64.mp4
+
+### Custom casual capturing
+A [short guide](https://sunset1995.github.io/dvgo/tutor_forward_facing.html) to capture custom forward-facing scenes and rendering fly-through videos.
+
+Below are two rgb and depth fly-through videos from custom captured scenes.
+
+https://user-images.githubusercontent.com/2712505/174267754-619d4f81-dd04-4c50-ba7f-434774cb890e.mp4
+
+### Features
+- Speedup NeRF by replacing the MLP with the voxel grid.
+- Simple scene representation:
+    - *Volume densities*: dense voxel grid (3D).
+    - *View-dependent colors*: dense feature grid (4D) + shallow MLP.
+- Pytorch cuda extention built just-in-time for another 2--3x speedup.
+- O(N) realization for the distortion loss proposed by [mip-nerf 360](https://jonbarron.info/mipnerf360/).
+    - The loss improves our training time and quality.
+    - We have released a self-contained pytorch package: [torch_efficient_distloss](https://github.com/sunset1995/torch_efficient_distloss).
+    - Consider a batch of 8192 rays X 256 points.
+        - GPU memory consumption: 6192MB => 96MB.
+        - Run times for 100 iters: 20 sec => 0.2sec.
+- Supported datasets:
+    - *Bounded inward-facing*: [NeRF](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1), [NSVF](https://dl.fbaipublicfiles.com/nsvf/dataset/Synthetic_NSVF.zip), [BlendedMVS](https://dl.fbaipublicfiles.com/nsvf/dataset/BlendedMVS.zip), [T&T (masked)](https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip), [DeepVoxels](https://drive.google.com/open?id=1ScsRlnzy9Bd_n-xw83SP-0t548v63mPH).
+    - *Unbounded inward-facing*: [T&T](https://drive.google.com/file/d/11KRfN91W1AxAW6lOFs4EeYDbeoQZCi87/view?usp=sharing), [LF](https://drive.google.com/file/d/1gsjDjkbTh4GAR9fFqlIDZ__qR9NYTURQ/view?usp=sharing), [mip-NeRF360](https://jonbarron.info/mipnerf360/).
+    - *Foward-facing*: [LLFF](https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7).
+
+
+### Installation
+```
+git clone git@github.com:sunset1995/DirectVoxGO.git
+cd DirectVoxGO
+pip install -r requirements.txt
+```
+[Pytorch](https://pytorch.org/) and [torch_scatter](https://github.com/rusty1s/pytorch_scatter) installation is machine dependent, please install the correct version for your machine.
+
+<details>
+  <summary> Dependencies (click to expand) </summary>
+
+  - `PyTorch`, `numpy`, `torch_scatter`: main computation.
+  - `scipy`, `lpips`: SSIM and LPIPS evaluation.
+  - `tqdm`: progress bar.
+  - `mmcv`: config system.
+  - `opencv-python`: image processing.
+  - `imageio`, `imageio-ffmpeg`: images and videos I/O.
+  - `Ninja`: to build the newly implemented torch extention just-in-time.
+  - `einops`: torch tensor shaping with pretty api.
+  - `torch_efficient_distloss`: O(N) realization for the distortion loss.
+</details>
+
+
+## Directory structure for the datasets
+
+<details>
+  <summary> (click to expand;) </summary>
+
+    data
+    ├── nerf_synthetic     # Link: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
+    │   └── [chair|drums|ficus|hotdog|lego|materials|mic|ship]
+    │       ├── [train|val|test]
+    │       │   └── r_*.png
+    │       └── transforms_[train|val|test].json
+    │
+    ├── Synthetic_NSVF     # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/Synthetic_NSVF.zip
+    │   └── [Bike|Lifestyle|Palace|Robot|Spaceship|Steamtrain|Toad|Wineholder]
+    │       ├── intrinsics.txt
+    │       ├── rgb
+    │       │   └── [0_train|1_val|2_test]_*.png
+    │       └── pose
+    │           └── [0_train|1_val|2_test]_*.txt
+    │
+    ├── BlendedMVS         # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/BlendedMVS.zip
+    │   └── [Character|Fountain|Jade|Statues]
+    │       ├── intrinsics.txt
+    │       ├── rgb
+    │       │   └── [0|1|2]_*.png
+    │       └── pose
+    │           └── [0|1|2]_*.txt
+    │
+    ├── TanksAndTemple     # Link: https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip
+    │   └── [Barn|Caterpillar|Family|Ignatius|Truck]
+    │       ├── intrinsics.txt
+    │       ├── rgb
+    │       │   └── [0|1|2]_*.png
+    │       └── pose
+    │           └── [0|1|2]_*.txt
+    │
+    ├── deepvoxels         # Link: https://drive.google.com/drive/folders/1ScsRlnzy9Bd_n-xw83SP-0t548v63mPH
+    │   └── [train|validation|test]
+    │       └── [armchair|cube|greek|vase]
+    │           ├── intrinsics.txt
+    │           ├── rgb/*.png
+    │           └── pose/*.txt
+    │
+    ├── nerf_llff_data     # Link: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
+    │   └── [fern|flower|fortress|horns|leaves|orchids|room|trex]
+    │
+    ├── tanks_and_temples  # Link: https://drive.google.com/file/d/11KRfN91W1AxAW6lOFs4EeYDbeoQZCi87/view?usp=sharing
+    │   └── [tat_intermediate_M60|tat_intermediate_Playground|tat_intermediate_Train|tat_training_Truck]
+    │       └── [train|test]
+    │           ├── intrinsics/*txt
+    │           ├── pose/*txt
+    │           └── rgb/*jpg
+    │
+    ├── lf_data            # Link: https://drive.google.com/file/d/1gsjDjkbTh4GAR9fFqlIDZ__qR9NYTURQ/view?usp=sharing
+    │   └── [africa|basket|ship|statue|torch]
+    │       └── [train|test]
+    │           ├── intrinsics/*txt
+    │           ├── pose/*txt
+    │           └── rgb/*jpg
+    │
+    ├── 360_v2             # Link: https://jonbarron.info/mipnerf360/
+    │   └── [bicycle|bonsai|counter|garden|kitchen|room|stump]
+    │       ├── poses_bounds.npy
+    │       └── [images_2|images_4]
+    │
+    ├── nerf_llff_data     # Link: https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7
+    │   └── [fern|flower|fortress|horns|leaves|orchids|room|trex]
+    │       ├── poses_bounds.npy
+    │       └── [images_2|images_4]
+    │
+    └── co3d               # Link: https://github.com/facebookresearch/co3d
+        └── [donut|teddybear|umbrella|...]
+            ├── frame_annotations.jgz
+            ├── set_lists.json
+            └── [129_14950_29917|189_20376_35616|...]
+                ├── images
+                │   └── frame*.jpg
+                └── masks
+                    └── frame*.png
+</details>
+
+
+
+## GO
+
+- Training
+    ```bash
+    $ python run.py --config configs/nerf/lego.py --render_test
+    ```
+    Use `--i_print` and `--i_weights` to change the log interval.
+- Evaluation
+    To only evaluate the testset `PSNR`, `SSIM`, and `LPIPS` of the trained `lego` without re-training, run:
+    ```bash
+    $ python run.py --config configs/nerf/lego.py --render_only --render_test \
+                                                  --eval_ssim --eval_lpips_vgg
+    ```
+    Use `--eval_lpips_alex` to evaluate LPIPS with pre-trained Alex net instead of VGG net.
+- Render video
+    ```bash
+    $ python run.py --config configs/nerf/lego.py --render_only --render_video
+    ```
+    Use `--render_video_factor 4` for a fast preview.
+- Reproduction: all config files to reproduce our results.
+    <details>
+        <summary> (click to expand) </summary>
+
+        $ ls configs/*
+        configs/blendedmvs:
+        Character.py  Fountain.py  Jade.py  Statues.py
+
+        configs/nerf:
+        chair.py  drums.py  ficus.py  hotdog.py  lego.py  materials.py  mic.py  ship.py
+
+        configs/nsvf:
+        Bike.py  Lifestyle.py  Palace.py  Robot.py  Spaceship.py  Steamtrain.py  Toad.py  Wineholder.py
+
+        configs/tankstemple:
+        Barn.py  Caterpillar.py  Family.py  Ignatius.py  Truck.py
+
+        configs/deepvoxels:
+        armchair.py  cube.py  greek.py  vase.py
+
+        configs/tankstemple_unbounded:
+        M60.py  Playground.py  Train.py  Truck.py
+
+        configs/lf:
+        africa.py  basket.py  ship.py  statue.py  torch.py
+
+        configs/nerf_unbounded:
+        bicycle.py  bonsai.py  counter.py  garden.py  kitchen.py  room.py  stump.py
+
+        configs/llff:
+        fern.py  flower.py  fortress.py  horns.py  leaves.py  orchids.py  room.py  trex.py
+    </details>
+
+### Custom casually captured scenes
+Coming soon hopefully.
+
+### Development and tuning guide
+#### Extention to new dataset
+Adjusting the data related config fields to fit your camera coordinate system is recommend before implementing a new one.
+We provide two visualization tools for debugging.
+1. Inspect the camera and the allocated BBox.
+    - Export via `--export_bbox_and_cams_only {filename}.npz`:
+      ```bash
+      python run.py --config configs/nerf/mic.py --export_bbox_and_cams_only cam_mic.npz
+      ```
+    - Visualize the result:
+      ```bash
+      python tools/vis_train.py cam_mic.npz
+      ```
+2. Inspect the learned geometry after coarse optimization.
+    - Export via `--export_coarse_only {filename}.npz` (assumed `coarse_last.tar` available in the train log):
+      ```bash
+      python run.py --config configs/nerf/mic.py --export_coarse_only coarse_mic.npz
+      ```
+    - Visualize the result:
+      ```bash
+      python tools/vis_volume.py coarse_mic.npz 0.001 --cam cam_mic.npz
+      ```
+
+| Inspecting the cameras & BBox | Inspecting the learned coarse volume |
+|:-:|:-:|
+|![](figs/debug_cam_and_bbox.png)|![](figs/debug_coarse_volume.png)|
+
+
+
 #### Speed and quality tradeoff
 We have reported some ablation experiments in our paper supplementary material.
 Setting `N_iters`, `N_rand`, `num_voxels`, `rgbnet_depth`, `rgbnet_width` to larger values or setting `stepsize` to smaller values typically leads to better quality but need more computation.