PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous

Xiuzhong Hu · Guangming Xiong · Zheng Zang · Peng Jia · Yuxuan Han · Junyi Ma

Beijing Institute of Technology

Our work has been accepted by IEEE Transactions on Intelligent Vehicles(TIV) and the paper link.

An early version for our work was: PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments.

The latest version on arxiv for our work is: PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous.

@ARTICLE{10438873,
  author={Hu, Xiuzhong and Xiong, Guangming and Zang, Zheng and Jia, Peng and Han, Yuxuan and Ma, Junyi},
  journal={IEEE Transactions on Intelligent Vehicles}, 
  title={PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments}, 
  year={2024},
  volume={},
  number={},
  pages={1-14},
  keywords={Laser radar;Three-dimensional displays;Robot sensing systems;Point cloud compression;Autonomous vehicles;Image reconstruction;Training;Neural Radiance Fields;3D Scene Reconstruction;Autonomous Driving},
  doi={10.1109/TIV.2024.3366657}}

[TOC]

Paper

The framework for our work:

Our PC-NeRF framework: (a) The hierarchical spatial partition divides the entire large-scale scene into large blocks, referred to as parent NeRFs. After multi-frame point cloud fusing, ground filtering, and non-ground point cloud clustering, a large block is further divided into point cloud geometric segments represented by a child NeRF. The parent NeRF shares a network with the child NeRFs within it. (b) In the multi-level scene representation, the surface intersections of the LiDAR ray with the parent and child NeRF AABBs and the LiDAR origin are used to divide the entire LiDAR ray into different line segments. The three losses on these line segments concurrently optimize the scene representation at the scene level, segment level, and point level, effectively leveraging sparse LiDAR frames. (c) For depth inference of each LiDAR ray, PC-NeRF searches in the parent NeRF AABB to locate corresponding child NeRF AABBs and then refines its inference in the child NeRF AABBs for higher precision.

The performance of our work:

Efficient 3D Reconstruction with PC-NeRF: Adapting to increased LiDAR frame sparsity through minimal training. The scene involves frames 1151-1200 from the KITTI 00 sequence, encompassing diverse elements like the ground, grass, walls, and vehicles. White dots in each subfigure depict individual LiDAR frame positions, and CD gauges 3D reconstruction accuracy, with smaller values indicating superior performance. As the proportion of LiDAR frames for training gradually decreases, signifying increased sparsity, PC-NeRF excels in sparse-to-dense 3D reconstruction, as evident in the last three rows of subfigures. Moreover, utilizing only 33% of LiDAR frames during training demonstrates advantages in both reconstruction quality and time consumption compared to using 50% and 80% of frames, as depicted in the first three rows of subfigures. More details are provided in Sec. IV-C.

Dataset partitioning and data preprocessing

We use frame sparsity to measure the sparsity of LiDAR point cloud data in the temporal dimension. Frame sparsity represents the proportion of the test set (unavailable during training) when dividing the LiDAR dataset into training and test sets. Increased frame sparsity implies fewer LiDAR frames for training and more for model testing, posing heightened challenges across various tasks. For further details, please refer to Sec. IV-A of our paper.

In our work, we show the results at eight frame sparsities of {20 %, 25 %, 33 %, 50 %, 67 %, 75 %, 80 %, 90 %}. In the following, we demonstrate the process of dataset partitioning and data preprocessing with the frame sparsity of 20% as an example. When the frame sparsity is 20%, it means that one frame every five frames is selected as the test set and the rest is used as the training set.

The frame sparsity setting is reflected in the following three locations:

(1) in data pre-process of train dataset: For example, in lines 52 to 56 of the data_preprocess/scripts/pointcloud_fusion.py file.

        if((j+1-3)%5!=0): # test set: hold one form every  5 scans, so train set is the remain
            count_train = count_train + 1
        else:
            continue            

For further details, please refer to the Data Pre-processing documentation.

(2) in train dataset load during training : For example, in lines 646 to 660 of the nof/dataset/ipb2dmapping.py

                file_path = os.path.join(self.root_dir, '{}.pcd'.format(j+1))          
                if((self.split == 'train') and (j+1-3-self.data_start)%5!=0):          # frame sparsity = 20%   
                # if((self.split == 'train') and (j+1-self.data_start)%4!=0):           # frame sparsity = 25%       
                # if((self.split == 'train') and (j+1-self.data_start)%3!=0):           # frame sparsity = 33%     
                # if((self.split == 'train') and (j+1-self.data_start)%2!=0):           # frame sparsity = 50%     
                # if((self.split == 'train') and (j+1-1-self.data_start)%3==0):      # frame sparsity = 67%      
                # if((self.split == 'train') and (j+1-1-self.data_start)%4==0):      # frame sparsity = 75%      
                # if((self.split == 'train') and (j+1-3-self.data_start)%5==0):      # frame sparsity = 80%      
                # if((self.split == 'train') and (j+1-5-self.data_start)%10==0):    # frame sparsity = 90%                          
                    print("train file_path: ",file_path)
                elif (self.split == 'val' and (j+1-3)%5==0):             
                # elif (self.split == 'val' and 0 ):    # Note: More stringent conditions can be set to make the validation set empty      
                    print("val file_path: ",file_path)          
                else:                        
                    continue      

(3) in test dataset load during testing : For example, in lines 1054 to 1062 of the eval_kitti_render.py

        for j in range(hparams.data_start, hparams.data_end):    
            if((j+1-3-hparams.data_start)%5==0):                # frame sparsity = 20%
            # if((j+1-hparams.data_start)%4==0):                 # frame sparsity = 25%         
            # if((j+1-hparams.data_start)%3==0):                 # frame sparsity = 33%                      
            # if((j+1-hparams.data_start)%2==0):                 # frame sparsity = 50%
            # if((j+1-1-hparams.data_start)%3!=0):              # frame sparsity = 67%
            # if((j+1-1-hparams.data_start)%4!=0):              # frame sparsity = 75%      
            # if((j+1-3-hparams.data_start)%5!=0):              # frame sparsity = 80%
            # if((j+1-5-hparams.data_start)%10!=0):            # frame sparsity = 90%

Train and Test

Some notes

Description of some important parameters

γ is a constant designed to represent the smooth transition interval on a LiDAR ray between the child NeRF free loss and the child NeRF depth loss. γ is a parameter that cannot currently be adjusted by bash scripts, but can be set in bash scripts via parameter passing. The expand_threshold in ./nof/render.py is used to adjust γ.

# (line 91~99)
            for i, row in enumerate(z_vals):
                expand_threshold = 2         #   
                interval = near_far_child[i]
                mask_in_child_nerf[i] = (interval[0]-expand_threshold <= row) & (row <= interval[1]+expand_threshold)      
                while (abs(torch.sum(mask_in_child_nerf[i]))==0): #                 
                    expand_threshold = expand_threshold + 0.01
                    mask_in_child_nerf[i] = (interval[0]-expand_threshold <= row) & (row <= interval[1]+expand_threshold)      
            weights_child = weights * mask_in_child_nerf.float()  # 
            z_vals_child= z_vals * mask_in_child_nerf.float()  #           

About frame sparsity

In the following experiments the frame sparsity is 20%, and the experiments for other frame sparsities are similar.

Data download

In data/preprocessing/,logs/kitti00/1151_1200_view/save_npy/split_child_nerf2_3/,logs/maicity00/maicity_00_1/save_npy/split_child_nerf2_3/,data_preprocess/kitti_pre_processed/sequence00/, part of the file exceeds the github recommended size for a single file, and the download link.

.
├── data
│   └── preprocessing
│       ├── kitti00
│       └── maicity00
├── data_preprocess
│   └── kitti_pre_processed
│       └── sequence00
└── logs
    ├── kitti00
    │   └── 1151_1200_view
    │       └── save_npy
    │           └── split_child_nerf2_3
    └── maicity00
        └── maicity_00_1
            └── save_npy
                └── split_child_nerf2_3

KITTI 00 sequence

OriginalNeRF

# train 
#     epoch = 1
bash ./shells/pretraining/KITTI00_originalnerf_train.bash

# eval
#     depth_inference_method = 1: use one-step depth inference
#     depth_inference_method = 2: use two-step depth inference
#     test_data_create = 0: use the test data created by ours
#     test_data_create = 1: recreate the test data
bash ./shells/pretraining/KITTI00_originalnerf_eval.bash

PC-NeRF

# train 
#     epoch = 1
bash ./shells/pretraining/KITTI00_pcnerf_train.bash

# eval
#     depth_inference_method = 1: use one-step depth inference
#     depth_inference_method = 2: use two-step depth inference
#     test_data_create = 0: use the test data created by ours
#     test_data_create = 1: recreate the test data
bash ./shells/pretraining/KITTI00_pcnerf_eval.bash

print metrics

#     inference_type: one-step: print the metric of one-step depth inference
#     inference_type: two-step: print the metric of two-step depth inference
#     version_id: version_0 (OriginalNeRF) or version_1 (PC-NeRF)
cd ./logs/kitti00/1151_1200_view/render_result/
python print_metrics.py

MaiCity 00 sequence

OriginalNeRF

# train 
#     epoch = 1
bash ./shells/pretraining/MaiCity00_originalnerf_train.bash

# eval
#     depth_inference_method = 1: use one-step depth inference
#     depth_inference_method = 2: use two-step depth inference
#     test_data_create = 0: use the test data created by ours
#     test_data_create = 1: recreate the test data
bash ./shells/pretraining/MaiCity00_originalnerf_eval.bash

PC-NeRF

# train 
#     epoch = 1
bash ./shells/pretraining/MaiCity00_pcnerf_train.bash

# eval
#     depth_inference_method = 1: use one-step depth inference
#     depth_inference_method = 2: use two-step depth inference
#     test_data_create = 0: use the test data created by ours
#     test_data_create = 1: recreate the test data
bash ./shells/pretraining/MaiCity00_pcnerf_eval.bash

print metrics

#     inference_type: one-step: print the metric of one-step depth inference
#     inference_type: two-step: print the metric of two-step depth inference
#     version_id: version_0 (OriginalNeRF) or version_1 (PC-NeRF)
cd ./logs/maicity00/maicity_00_1/render_result/
python print_metrics.py

Acknowledgements

The following codes have helped us a lot in our work:nerf-pytorch, ir-mcl , shine_mapping