NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation

Summary

Point Cloud Interpolation (PCI) is the task of predicting intermediate point cloud features from a sparser representation, often to construct point cloud representations for intermediate time frames. Contrary to interpolation in other fields, point clouds are largely unstructured and do not preserve consistent information across different sequence frames, and thus cannot be addressed adequately with classical interpolation functions such as linear interpolation. To properly represent point clouds in a latent space for learned approaches, a method that respects the volumetric nature of point distributions is crucial for minimising noisy artifacts caused by the unordered nature of point clouds. This paper proposes three new components of PCI learning architectures that address this necessity. First, a Gaussian clustering function is used to group point sets into distinct regions, which are less prone to misrepresenting unintended variations in point cloud data, and facilitates a necessary structure for PCI. To my understanding, with the sparse latent representations, interpolation is achieved by first using Radial Basis Functions (RBF) to provide an initial smooth estimate of the interpolated Gaussian cluster sequence, followed by a learned graph-based network to determine a final interpolation based on the composition of RBF sequences from every cluster.

Strengths

The paper is technically sound with strong results against state-of-the-art methods in PCI of dynamic environmental LiDAR scenes. The methodology presentation is largely clear and well structured. A detailed ablation study is provided to demonstrate the importance of each component for effective PCI based on standard Chamfer Distance and Earth Mover Distance metrics.

Weaknesses

Section 1: Introduction - The introduction writing structure is quite unconventional, and repeats itself word for word in the contributions statement. I suggest the introduction of the proposed method be explained in more detail with regards to its overall architectural structure, to incorporate a consistent flow with the presentation of the method's components. - The mentioned challenges of PCI are sporadic and inconsistent with the way PCI is introduced as a research problem. The use of a mathematical formula to introduce PCI takes away from the intended motivation of PCI-centric research, and is therefore difficult to link to each challenge. e.g. If solving Eq.(1) is the goal, what does the representation of "multiple unordered point clouds" have to do with it? This is unclear with the current presentation. - Figure 1 is a mess of graphs and qualitative examples that belong as separate (sub)figures in the Experiments section. The introduction of Figure 1 in the beginning of the work is inappropriate. What is the expected ground truth for the right side figures? It is unclear what the right side figure is trying to demonstrate. Section 2: Related works - Like the introduction, the presented explanation is centred around explaining mathematical formulae, rather than intuitively presenting technical problems and expressing it as formulae if necessary. It is generally unnecessary for the formula to be understood in order to analyse the current state of research for each subsection. For this reason, by linking the motives of past research to solving an otherwise arbitrary formula, the flow of the related works section is unclear. - L81: "for efficient:" appears to be in error. Section 3: Methodology - 1), 2), 3) are not labelled in the diagram itself, and thus can be difficult to associate. - Figure 3 fails to visualise the purpose of each component, i.e. RBF Activator, Gaussian/Feature Interpolators, in the proposed method. It is generally expected that a flowchart is used to both illustrate the information flow of the system, and provide labels for the purpose, i.e. I/O, of each component. Mathematical equations are highly ineffective for this.

Questions

1. Figure 2: Is it correct to call the output of the soft-clustering process "spheres"? The figure appears to describe "ellipsoids" rather than consistent radius spheres. 2. Outlier removal in Section 4.2/Table 2 appears to improve the results of the method quite significantly. How does it affect other state-of-the-art methods?

Rating

6

Confidence

4

Soundness

3

Presentation

1

Contribution

3

Limitations

Yes, limitations of the method are discussed briefly in the manuscript regarding its applicability in practical scenarios.

Summary

The paper presents a novel model, “NeuroGauss4D-PCI,” for point cloud frame interpolation, a popular but challenging 3D computer vision task in real-world scenarios such as Lidar point cloud densification. The model outperforms existing PCI methods on the most popular benchmark datasets, DHB and NL-Drive, showcasing scalability to tasks like auto-labeling and point cloud densification.

Strengths

- The paper is well written and well-organized, a good review of related works supports the motivation, the experiment is well designed, and the results and additional information are satisfied. - Although I’m not an expert in developing such algorithms in detail, as a user from the application side, I can understand that using 4D Gaussian deformation fields and temporal radial basis function Gaussian residual modules to capture complex spatiotemporal dynamics is a novel approach.

Weaknesses

- There are no details of the experiment platform or discussion of the computational efficiency of the proposed method. - The potential of NeuroGauss4D-PCI seems significant. But as we all know, NeuralPCI was said to be the best model for PCI until now. A more detailed comparison with this model could be crucial in providing valuable insights and strengthening the paper's contribution to the field.

Questions

I don’t have additional detailed questions for this paper, as I may not be good enough to understand all the details of the proposed algorithm.

Rating

8

Confidence

3

Soundness

4

Presentation

3

Contribution

3

Limitations

I wonder if it is convincing that all are testing their work on only two open datasets used in this paper.

Summary

The paper introduces NeuroGauss4D-PCI, a model designed to address the challenges of point cloud interpolation (PCI) by using Gaussian soft clustering and a 4D neural field to model complex non-rigid deformations in dynamic scenes. The model excels in capturing spatial and temporal dynamics from sparse data, showing superior performance on object-level and large-scale datasets.

Strengths

It is interesting to utilize Gaussian clustering and 4D neural fields for solving PCI problems.

Weaknesses

1. The color scheme of the figures in the article is very poor, making many details difficult to discern. 2. Point cloud interpolation is a relatively niche direction in point cloud analysis. The author should more clearly emphasize the significance of studying this issue. 3. The most related work reported in this work might be the CVPR 23 paper Neuralpci. Does that mean no more recent related works?

Questions

1. How does NeuroGauss4D-PCI ensure robustness against varied data sparsity in real-world scenarios? 2. Can the model handle rapidly changing dynamic scenes, such as explosions or quick animal movements? 3. What are the computational costs associated with training and inference, and how do they compare to simpler models? 4. How does the model perform under conditions of significant occlusion or sensor noise?

Rating

4

Confidence

4

Soundness

3

Presentation

1

Contribution

2

Limitations

1. The complex interactions between Gaussian fields and neural networks may hinder understanding and tuning of the model. 2. The high computational demand may limit deployment in time-sensitive applications.

Summary

This paper proposes a novel method for point cloud interpolation, which aims to address complex non-rigid scenarios. It turns point clouds into 3D Gaussians via iterative soft clustering, and then utilizes several 4D spatio-temporal modules to fuse latent features with neural fields. Specifically, this paper employs the temporal radial basis function for Gaussian interpolation, the graph convolutional network for feature extraction and the attention module for feature fusion. Comprehensive experiments demonstrate the leading performance across indoor and outdoor datasets, along with a thorough ablation study for the proposed components.

Strengths

- It is the first to introduce 3D Gaussians for point cloud interpolation, which shows great performance in modeling non-rigid deformations and non-linear trajectories. - The proposed 4D Gaussian deformation fields leverage temporal graph convolutions for spatio-temporal feature aggregation. - The experiments are conducted on multiple datasets, including indoors and outdoors. It is also compared on the scene flow benchmark to demonstrate its effectiveness. And the ablation study is comprehensive for each proposed module.

Weaknesses

- The proposed method is overly complex and redundant, which shows a trivial combination of existing modules and limited contribution. Moreover, the proposed modules lack strong motivation and necessity, and the interpretability of the intermediate features is difficult to clarify. - According to the Supplementary Material, nearly two hours of optimization time is required for just four frames of input, which is much more than the previous method. The problem may lie in the design of proposed method, which is not suitable for an optimization-based approach, but rather a learning-based approach. For example, some feature extraction or fusion may not be meaningful or necessary for a single sample fitting.

Questions

- This paper may not be related to the work of 3DGS series. It just uses 3D Gaussians but has nothing to do with "splatting". Also, the proposed method is more similar to the super-point concept or the Gaussian Mixture Model in clustering and should be discussed. - Does DGCNN use pre-trained models or is it trained from scratch? Retraining DGCNN for each optimization can be time consuming and may not be meaningful for fitting individual samples. - In the Temporal RBF-GR Module, the 3D Gaussian needs to be interpolated, so how to ensure the correspondence? Also, is it feasible to visualize the Residual Gaussian to prove its reasonableness and validity? - Quantitative comparisons of efficiency with other methods. It would be better to analyze the training/inference time for each module. In addition, can the proposed method be extended to more frames or more points of input, and what is the corresponding efficiency? typo: - L136 - "Table4.2"? - missing arrows of ACC_S and ACC_R in Table 3

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

2

Limitations

- The entire framework can be further simplified and improve its efficiency. - Additional supervision or constraints can be introduced for 3D Gaussians rather than only points. - Use more challenging datasets beyond DHB. In Table 1, the results for both Squat and Swing sequences are 0.00, which may need to retain higher precision or change to other datasets.

Summary

This paper proposes a method, NeuroGauss4D, to tackle the problem of 4d point cloud interpolation. NeuroGauss4D consists of the following 5 components: 1. "Iterative Gaussian Soft Clustering": a module to encode the scene to Gaussian representation with DGCNN features (map: X, Y, Z, T -> Gaussians(Mu, Cov, Feat)). 2. "4D Neural Field": a neural field with Fourier features and MLP to capture spatio-temporal features in latent space (map: X, Y, Z, T -> latent). 3. "Temporal RGB-GR": a module with temporal radial basis functions to interpolate Gaussian parameters in continuous time (map: Mu, Cov, Feat, T -> ΔMu, ΔCov, ΔFeat, ΔT). 4. "4D Gaussian Deformation Field": a module that take Gaussian parameters (Mu, Cov, Feat) and the latent features as input to predict the motion and feature deformation field (map: Mu, Cov, Feat, T -> deformation field). 5. "Fast-LG-Fusion": an attention mechanism that fuses the latent features (FL) and the geometric features (FG, from the deformation field) to predict the point cloud at the target time step (map: FL, FG, T -> point cloud).

Strengths

1. This method shows the application of dynamic Gaussian representation to the problem of 4D point cloud interpolation. 2. This method outperforms previous methods on the set of evaluations provide by the author.

Weaknesses

1. This method shows the application of dynamic Gaussian representation to the problem of 4D point cloud interpolation, which is a good idea. However, the execution of the idea seems to make the method overly complex. NeuroGauss4D-PCI's pipeline contains 5 different components, where a mixture of representations are used. Besides more hand-tuning for hyperparameters and longer training time, this complexity could make it hard to interpret the fundamental principles and the key components behind the full model. 2. It seems that the method is not very efficient. For example, the "Iterable Gaussian Soft Clustering" procedure shown in Algorithm 1 is only capable of taking one timestamp of point cloud (N, 3) as input, which means that this has to be done for each timestamp of the point cloud sequence. 3. Similar to the point above, the method might be limited on the size of the point cloud sequence that it can handle. For example, line 218 says "we sample the input points to 1024 for object-level scenes and 8192 for autonomous driving scenes". A point cloud of 1024 points in autonomous driving scenes is very small, which could limit the method's applicability to real-world scenarios. The authors can provide more information on how the method will perform when we have a larger point cloud size or a longer sequence of point clouds. 4. The author provides rich information about the method, which is good, but the clarity of the writing can be improved. Some of these are mentioned in the "Questions" section, and it would be nice to include them in future versions of the paper. There are other inconsistencies in the paper, for example: - Equation 1 uses 0-based indexing, but line 29 uses 1-based indexing. - Line 109 has an extra double quote. - Line 117 says "Iterative Gaussian Cloud Soft Clustering" but the Figure 2 uses "**Iterable** Gaussian ...". - Equations shall be written as part of a sentence, not as separate sentences (Eq. 5, 8, 9, 10, 11; and also Eq. 4).

Questions

1. It would be good to clarify in paper the exact meaning of "Frame-1, Frame-2, and Frame-3" in Table 2. Does it mean that we fit the scene with frame {0, 4, 8, 12, ...}, and then we evaluate on frame {1, 2, 3, 5, 6, 7, 9, 10, 11, ...}, and we call frame {1, 5, 9, ...} as Frame-1, frame {2, 6, 10, ...} as Frame-2, and frame {3, 7, 11, ...} as Frame-3? 2. In Table 5, when we say "4 frames of point clouds sampled at regular 3-frame intervals", does it mean that for each scene, we only train on 4 frames {0, 4, 8, 12} and we evaluate on 9 frames {1, 2, 3, 5, 6, 7, 9, 10, 11}? If this is the case, why do we only train on these 4 frames but not fit the full point cloud sequence of the scene in the raw dataset? 3. In line 456, when we say "initial frame at time step 0 and the final frame at time step 1", what do the "0" and "1" refer to? For example, in equation 1, it is clear that there can be time steps larger than 1, e.g. time step 4, 8, etc. Is the "0" and "1" in line 456 referring to some form of normalized time step? So, what exactly is the time step gap (in seconds) between the training frame and evaluation frames? 4. From equation 1, it seems that the method only fits one model for one whole scene, which is good. Can you confirm this? 5. In line 476 we report the "average training time is ∼ 1.31 seconds per iteration". Per my understanding, this "iteration time" means the training iteration. It is not clear if the time includes "Iterative Gaussian Cloud Soft Clustering" (which has to be done for each time step), and other feature extraction steps. It would be more clear to give a high-level timing number, for example, could you report "how long does it take to fit a full of ~X training frames each containing ~Y number of points"?

Rating

5

Confidence

4

Soundness

3

Presentation

2

Contribution

2

Limitations

Yes, the authors have discussed the above.