FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors
Authors: Shuai Liu
Boyang Li
Zhiyu Fang
Mingyue Cui
Kai Huang
Venue: NeurIPS 2024 poster (2024)
Keywords: Explainable artificial intelligence
visual explanation
3D object detection
Primary area: interpretability_and_explainability

Abstract: LiDAR-based 3D object detection has made impressive progress recently, yet most existing models are black-box, lacking interpretability. Previous explanation approaches primarily focus on analyzing image-based models and are not readily applicable to LiDAR-based 3D detectors. In this paper, we propose a feature factorization activation map (FFAM) to generate high-quality visual explanations for 3D detectors. FFAM employs non-negative matrix factorization to generate concept activation maps and subsequently aggregates these maps to obtain a global visual explanation. To achieve object-specific visual explanations, we refine the global visual explanation using the feature gradient of a target object. Additionally, we introduce a voxel upsampling strategy to align the scale between the activation map and input point cloud. We qualitatively and quantitatively analyze FFAM with multiple detectors on several datasets. Experimental results validate the high-quality visual explanations produced by FFAM. The code is available at \url{https://anonymous.4open.science/r/FFAM-B9AF}.

FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors
Shuai Liu, Boyang Li, Zhiyu Fang, Mingyue Cui, Kai Huang"
School of Computer Science and Engineering, Sun Yat-sen University (liush376@ mail2, liby83@ mail, fangzhy9@mil @mail2, cuimy @mail2, huangk36 mail) .syuu.edu.u
Abstract
LiDAR-based 3D object detection has made impressive progress recently, yet most existing models are black-box, lacking interpretability. Previous explanation approaches primarily focus on analyzing image-based models and are not readily applicable to LiDAR-based 3D detectors. In this paper, we propose feature factorization activation map (FFAM) to generate high-quality visual explanations for 3D detectors. FFAM employs non-negative matrix factorization to generate concept activation maps and subsequently aggregates these maps to obtain a global refine the
visual explanation. To achieve object-specific visual explanations, global visual explanation using the feature gradient of a target object. Additionally, introduce voxel upsampling strategy to align the scale between the activation map and input point cloud. We qualitatively and quantitatively analyze FFAM with multiple detectors several datasets. Experimental results validate the high-quality visual explanations produced by FFAM. The code is available at https: //tpt//ggtttt/tt// Cactuuu..oom/S.. cww/Say/L///aaac SaFSSaaLL/FFM.... git
1 Introduction
In recent years, there has been rapid development LiDAR-based 3D object detection [3 32
38 [1]. making it widely utilized in autonomous driving, industrial automation, and robot navigation. However, existing detection methods predominantly rely on deep neural networks with highly nonlinear and complex structures. Essentially, these models be considered "black box" systems. Such opaque modeling techniques hinder users from fully trusting the detection models, particularly sensitive and high-risk domains. Consequently, understanding the decision-making process of these inherently opaque models is urgently needed.
34
Visual explanation methods 120 11
[6] have gained widespread adoption for analyzing models based on deep neural networks. These methods generate saliency maps that highlight the crucial elements influencing the model's decision within the input map. Perturbation- based [6],
33 15]
class activation map (CAM)-based [4]] 20, I. and gradient- based (28 24 25] methods are the three main categories of visual explanation methods. However, these methods primarily focus image-based models and are not directly applicable to point cloud-based models. The pioneering work in analyzing 3D detectors OcсAM (19), which extends D-RISE (16) to perturb point clouds. As perturbation-bant measures the change in model predictions. However, the large number of inference calculations makes OccAM computationally intensive, and the sampling number easily impacts the quality of
approach, OccAM first randomly samples numerous sub-point clouds and
generated saliency maps.
Interpreting 3D detectors presents three key challenges. First, point clouds are inherently three- dimensional (3D). It is essential to generate corresponding 3D saliency maps for accurate interpreta-
"Corresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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tion. However, existing methods, such as popular maps from the network's last layer to generate 2D saliency maps. Second, the explanation method for 3D detectors should provide detailed explanations for individual objects of interest. Yet, most existing methods yield class-specific saliency maps, which means they cannot focus on explaining a specific detection object. Lastly, point clouds are sparsely distributed in 3D space, rendering linear interpolation employed by many image-based explanation methods ineffective.
techniques, primarily utilize activation
To address the aforementioned chal- lenges, this paper introduces a feature factorization activation map (FFAM) to obtain visual explanations for 3D detectors. Specifically, to solve the first challenge, FFAM leverages the 3D feature maps within the 3D back- bone (36) of detectors, rather than re- lying on the bird's eye view (BEV) feature maps from the last layer. Drawing inspiration from DFF 3) we employ non- non-negative matrix factoriza- tion (NMF) [I]] to uncover latent se- mantic concepts within these 3D feature maps. Typically, point features with effective detection clues in 3D detectors contain richer semantic concepts. Thus, aggregate concept activation maps generated by NMF to obtain a global concept activation map that highlights important points, as shown in Tigure[aa). To address the second challenge of obtaining opject-specific saliency maps, utilize the gradients of the 3D feature map, generated by an object-specific loss, to refine the global concept activation map. This process is illustrated in Figure [b), showcasing the desired effect. To tackle the final challenge, we introduce voxel upsampling strategy to sample values from sparse neighbors, ensuring accurate saliency map generation.
(a)
(b)
Figure I: Visualization of FFAM outputs. (a) global concept
activation map and (b) object-specific activation map.
We compare our FFAM with the current state-of-the-as the-trt method OccAM [9]. as well as other image- based explanation methods including Grad-CAN (20) and ODAM (3). We conduct experiments on the KITTI 7 and Waymo Open 26) datasets, employing detectors such as SECOND (36] and CenterPoint 37 The qualitative and quantitative results demonstrate that our FFAM significantly outperforms the previous methods. The contributions of this work can be summarized as follows:
We propose a feature factorization activation map (FFAM) method to obtain high-quality visual explanations for 3D detectors.
We first introduce NMF in explaining point cloud detectors. By aggregating different concept activation maps, we obtain a global concept activation map that highlights points with significant detection clues.
We utilize feature gradients of an object-specific loss refine the global concept activation map, enabling the generation of object-specific saliency maps.
A voxel upsampling strategy is proposed to upsample sparse voxels, thus aligning the scale between the activation map and input point cloud.
2 Related Work
Explanation Methods for Image-based Models. Existing explanation methods primarily focus on image classification models. Perturbation-based methods [27]
15 31 are widely used for interpreting image classification models. The core idea is to assign importance scores to perturbed feature components by disturbing the model's input and observing the output changes. CAM- I D generate saliency maps by linearly combining activation maps from intermediate layers, weighted by their respective contributions. Some approaches (e.g. Score- CAM
based methods 41 20
[33] and Ablation-CAN (18) combine perturbation- and CAM-based ideas to eliminate dependence on backpropagation gradients. Additionally, gradient-based explanation methods 22
use gradients to quantify input impact on network predictions. Higher gradient values indicate greater importance of the corresponding input elements. Moreover, feature factorization techniques like principal component analysis (PCA) and non-negative matrix factorization (NMF) can uncover latent
28
24 25
patterns in deep features. DFF (3] employs NMF to localize semantic concepts within images.

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Compared to explanation methods for classifiers, only a limited number of approaches investigate explanations for object detection models. The aforementioned methods generate class-specifie explanations, which are not feasible for object detection models. D-RISE [16] employs perturbation strategy to generate instance-specific explanations by defining a detection similarity metric. In [35), a directed acyclic AND-OR Graph (AOG) is utilized to uncover latent structures in object detectors. G-CAME [14] combines activation maps with a Gaussian kernel of gradients to generate saliency map for predicted bounding box. ODAM
[39] employs pixel-wise gradients of target object to weigh the activation maps, thereby producing an instance-specific saliency map.
Explanation Methods for Point Cloud -loudd Models. In contrast to explanation methods for image- based models, the field of explanation for point cloud-based models is relatively underdeveloped. Existing methods primarily focus on point cloud classification models. For instance. 40] utilizes the loss gradient to measure the contribution of each point in the classifier. Similarly, g applies gradient-based strategy to analyze the intermediate features of the network. Another approach (29 combines a generative model with the activation maximization method 6 to obtain global explanation for point cloud networks.
Research on the explanation of 3D detectors is still quite limited. One perturbation-baase method, OccAM [9], estimates the importance of individual points by testing the model with randomly generated subsets of the input point cloud. However, the scale of points 3D space is considerably large, and the distribution of points acquired through LiDAR varies with distance. These aforemen- tioned issues result in the following challenges for perturbation based methods: (1) It is difficult to exhaustively perturb the point cloud, limiting the quality of visual explanations; (2) Generating ample random subsets of points requires multiple iterations, thereby reducing efficiency. Taking inspiration
from feature factorization techniques [3] and gradient-based approaches (28) 14 (39, we propose an explanation method called FFAM. aims to efficiently generate high-quality saliency maps for 3D detectors.
LiDAR-based 3D Object Detection. These methods can be categorized into two main groups: one- stage and woostage detectors. One-stage detectors typically employ simple network architectures to achieve high speeds. For instance, SECOND (36) efficiently encodes sparse voxel features using a proposed 3D sparse convolution technique. PointPillars [10] divides point cloud into pillar voxels, eliminating the need for 3D convolution layers and achieving fast inference speed. VoxelNeXt introduces fully sparse convolution network that eliminates the requirement for aparse-to-dente conversion. Two-stage detectors generally incorporate an additional stage to refine proposals generated by one-stage network. PointRCNN [22] utilizes PointNet++ [17] to generate proposals
from raw points and then refines the bounding boxes in the second stage. PV-RCNN [21 combines
voxel-based proposal network with point-based refinement network. CenterPoint (37 extracts
point features from the surface centers of proposal bounding boxes for refinement. Voxel R-CNN (5)
utilizes voxel features from the 3D backbone to refine the proposals. Our explanation method FFAM
is adaptable to both one- and two-stage detectors without being limited by the detector type. We
primarily conduct experiments on widely used detectors, including the one-stage detector SECOND and the two-stage detector CenterPoint.
3 Method
The goal of visual explanation for 3D detector f is to produce saliency map for each detection. Given a point cloud P E R.Nxx the saliency map consists of N values presenting the importance of each point in P for a detection d which consists of bounding box, confidence score and category label. We denote a detection d as follows:
d= [r, 2, h, 8,
where (z, y, denotes the center location, (1, w, h) represents the object size (i.e., length, width and height), s and e indicate the confidence score and category label, respectively.
We propose FFAM to produce saliency maps in point cloud format for 3D detectors. The overview of our method is illustrated in Figure [ It can be divided into three phases as follows: (1) Feature
factorization (Sec. 3.1); h; (2) Gradient weighting (Sec. 3.2 (3) Voxel upsampling (Sec. 3.3
3

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Feature map
Backbone
Head
FFWW
Weights
Concepts weights
Concept bank
aggregation
Detections
Voxel upsampling
Gradients
d V,,, M, A S,
aggregation
Object-spectic
gradients
Figure 2: Overall framework of our FFAM which can generate an object-specific saliency map for detection d,
3.1 Feature Factorization Activation Map
Matrix factorization is widely used in fields such as recommendation systems, image processing, and natural language processing to extract potential features and reduce dimensionality. Non-negative matrix factorization (NMF) as a classical matrix factorization algorithm approximates matrix by decomposing it into the product of two non-negative matrices. With this decomposition, NMF can discover potential patterns and conceptions in the raw matrix and extract the most important features. Given a non-negative matrix A E Rmxx, NMF retrieves an approximation mR as follows:
nog-nvgative
NMF(A) argminn | A A 2 F²
(2)
S.L. À HW, Viy, Hoj, Wij 2 0,
where RExT and W E x denote two non-negative matrics, r is a predefined parameter indicating the number of latent concepts in matrix A. Each row W, E R²(1 < S r) of W represents a concept vector. These concept vectors are typically well-interpreted and associated with object-part features, such wheels, doors, roofs, and on, following the non-negative additivity property of Wj. Furthermore, each row H; R" (where 1 S i i m) of matrix H represents the combination weights of different concept vectors in W. Combining these concept vectors using the weights H,, we obtain the i-th row feature of matrix Á.
In this paper, we employ non-negative matrix factorization to handle the voxel feature map within the 3D backbone of detectors. Typically, voxel features that contain crucial detection clues tend to activate more concepts (e.g., license plates, car fronts, car edges) detectors. As result, aggregating all weights in H, indicates the significance of the i-th voxel feature in the voxel feature map, as demonstrated in Figure [a).
Specifically, given voxel feature map F R xd where M represents the voxel number and d denotes the channel number, voxel feature F; E Rdd in F can be factorized as follows:
Σ H.
Further, we obtain the global concept activation map V by aggregating concept weight matrix as follows:
V
H jj
(4)

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where H., denotes j-th column of H. The resulting V emphasizes points with multiple activated concepts from a global perspective. And due to the downsampling operation in the detection network, the granularity of V is typically coarse. Therefore, further processing is required to obtain an object-specific and fine-grained activation map, as described in Sec. 3.2 and Sec. 3.3
Object-Specific Gradient Weighting
In a 3D detector, the output contains large number of detections. To obtain object-specific activation map, we establish a loss function a specific detection. Specifically, given detection create a baseline detection d, calculate the loss f:
t | d6 |l
(5)
For simplicity, we use the Ll loss function and set all values in d, equal to 0. Then we obtain the gradient map R-M of the feature map F:
0R G DF
Considering an optimization process from to de the matrix denotes the optimal direction for reducing the loss. If we iteratively update the feature map F' based on the gradient map G, the information related to the detection d will be diminished. Alternativly. by utilizing G, we can identify the locations in the feature map F that contain clues about d. Consequently, object-specific activation map M for d can be obtained as follows:
d Σ
w
G
M 1 (w) (V))
where G. E RM refers to the k-th column of G. while represents the normalization operation, and @ denotes element- wise multiplication. By modifying the loss function specific attribute p in detection d, we can examine the specific points on which the detector concentrates when predicting attribute p.
3.3 Voxel Upsampling
Due to downsampling operations in 3D detection networks, the scale of the activation map is typically smaller than that of the input point cloud. Consequently, upsampling the activation map M becomes necessary. However, unlike 2D images, linear interpolation for upsampling 3D sparse voxels presents challenges. To address this, we draw inspiration from the voxel query technique proposed by (5) and introduce a voxel upsampling strategy for 3D sparse voxels. Specifically, we define the voxel size s, and the ranges of the point cloud for three axes as [zz, 2r.. (yt, yrl, and [2t, 2,] respectively. Given point (x, y, 2). we calculate the coordinate (Ip. Up 2p) of voxel Up to which belongs as follows:
Zp
Upp
2p
(8)
Then we query neighbor voxels on activation map M for using Manhattan distance to control the query range:
d(up. u) |zn xpl Vnn ypl 12n 2pl-
where (In, Nn. 2n) is the coordinate of an neighbor voxel v, d(., .) is the Manhattan distance between two voxels. We sample up à neighbor voxels within distance threshold. Finally, the salience score sp of point is calculated follows:
5

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high
OccAM
low
FFAM
Salieney maps for SECOND KITTI dataset.
OccAM
FFAM
(b) Saliency maps for CenterPoint on Waymo Open dataset.
Figure Saliency maps for SECOND (36) and CenterPoint (37]. The green bounding boxes
indicate the detected objects, while warmer colors (using the turbo colormap) represent higher point contributions these detections. The crops are provided for visualization purposes only.
= Σ (d(Cp u)) (d((pp, Mys
(10)
EVEN DEN
where N is the of neighbor voxels, V denotes a Gaussian kernel with a standard normal distribution, M, represents the value of voxel activation map M.
Experiments
In this section, we compare our FFAM with existing explanation methods, including Grad-CAM 20 and ODAM [39, for image-based models, well as with OccAM [19], the state-of-the-an explanation method for point cloud-based models. We adopt two datasets for evaluation: KITTI ( widely used autonomous driving dataset, and Waymo Open 26). containing complex multi-object scenes. For KITTI, experiments are conducted on SECOND 36 For Waymo Open, we mainly evaluate on CenterPoint [37]. The experiments are run using PyTorch and RTX 3090 GPU. The hyperparameters of detectors and OccAM remain consistent with their official implementations. The parameter r' used NMF is set 64. The Manhattan distance threshold and parameter & in voxel upsampling are set to and 16, respectively. We use the 3D feature map from the third block of the 3D backbone as FFAM input. Hyperparameters analysis and ablation study are in App. A. and App. A.4 respectively.

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d
(x)
h
Figure 4: Average saliency maps for different object attributes. (z, denotes the center of predicted object. I, w, h, and represent the length, width, height, rotation angle and classification score of predicted object, respectively. d indicates the combination of all attributes.
4.1 Qualitative Results
To verify the interpretability of our FFAM, we visualize explanations for some objects. We also visualize the average saliency maps of different categories for specific object attributes study the latent pattern of 3D detectors.
Visualization of Saliency Map. We compare the visual explanations generated by FFAM and OccAM [19 for cars, pedestrians, and cyclists in Figure3Ja) These detection results are obtained by SECOND 36 detector trained on KITTI (7]. OccAM exhibits significant background noise due its random masking mechanism. In contrast, our FFAM demonstrates strong ability to generate clear, distinct object-specific saliency maps. We observe the detector also captures relevant clues from the background and neighboring objects. Furthermore, we compare saliency maps generated by FFAM and OccAM on Waymo Open [26] using the CenterPoint (37] detector, as shown in Figure ()). The saliency maps produced by OccAM struggle to focus on the intended object for interpretation. They have more highly salient points distributed on the background compared to KITTI. We attribute this discrepancy to the larger number of points in Waymo Open samples, challenging the random masking mechanism to sample diverse point masks effectively. Conversely, our FFAM consistently generates high-quality saliency maps Waymo Open.
Average Saliency Map. To further explore the detection mode of detectors and verify the inter- pretability of FFAM. we average the saliency maps of specific classes, including cars, pedestrians and cyclists. We use SECOND trained on KITTI 77] as the detector. To accomplish this, we first scale all boxes and associated points to a uniform size and then align them with respect to their center and rotation angle. Next, we voxelize the resulting point cloud and calculate the average saliency values of individual points within each voxel. The resulting saliency maps for different object attributes are presented in Figure
As depicted in the first two rows of Figure 4 the detector primarily identifies and localizes car objects based on the points located the four comner of the car. By analyzing features from these points, the detector infers various attributes of a car, such as its center location, length, width, rotation angle, and classification score. This can be attributed to the fact that car objects are often incomplete in outdoor point clouds, and their corners are frequently scanned by LiDAR and used as key features. However, there is special case, as shown in the first two rows of the penultimate column of Figure 4 where the height attribute is predicted primarily based on the points at the top of the car. As illustrated in the third row of Figure 4 the detector predicts pedestrian objects mostly based on the points distributed on the head and shoulder regions. Additionally, the detector recognizes cyclist objects mainly based on the points distributed on the head and back of the human body, as shown in the last row of Figure 4 Furthermore, we observe that the prediction of cyclist height heavily relies on the points distributed on the head, similar to the prediction of car height. Additional average saliency maps of other detectors are in App. A.2
7

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0.9
Avergelol 1oU
0.7
Avenge loU
Method
VEA
f
All
Car
Ped.
Cyc.
0.
Grad-CAM
0.015
0.015
0.018
0.013
04
ODAM
0.179
0.163
0.280
0.233
OccAM
0.064
0.063
0.080
0.042
CEAN
FFAM (Ours)
0.391
0.363
0.543
0.515
01 02 0.004 07000900
12203 04 04 080910
Sieg
Step (b)
Table 1: Comparison of visual explanation accuracy metric for different categories. 'all' denotes the three categories are included.
(a)
Figure 5: AUC diagrams for Deletion and Inser- tion. Average IoU vs. (a) Deletion steps and (b)
Insertion steps.
Deletion
|
Insertion
Method
All
Car
Ped.
Cyc.
All
Car
Ped.
Cyc.
Grad-CAM
0.335
0.373
0.137
0.129
0.797
0.821
0.688
0.725
ODAM
0.134
0.138
0.122
0.098
0.885
0.902
0.785
0.828
OccAM
0.286
0.311
0.146
0.167
0.863
0.880
0.761
0.790
FFAM (Ours)
0.071
0.068
0.098
0.078
0.907
0.923
0.806
0.854
Table 2: AUC for Deletion and Insertion curves.
The results of different categories are reported. 'all"
means the combination of the three categories.
4.2 Quantitative Results
We adopt Deletion, Insertion 15 games (PG) to evaluate our FFAM. SECOND trained on KITTI is used as the baseline detector. Following previous work [39]. we use the well-detected objects in the evaluation dataset as the subjects to be explained. In particular, a predicted object is considered well-detected if the IoU between it and its ground truth is greater than [0,7, 0.5, 0.5] for car, pedestrian and cyclist classes, respectively. See App. A.3 for results on Waymo Open.
16 39
visual explanation accuracy (VEA) 13 and Pointing
Deletion and Insertion. Deletion and Insertion are widely used to evaluate explanation methods for image-based detection models [16] 39.. Deletion involves sequentially removing highly salient elements from a scene, measuring the rate model predictions diverge from the original. Insertion progressively adds salient elements to an empty scene, measuring how quickly predictions approach the original. Considering the similarity between pixels in an image and points in point cloud, we employ Deletion and Insertion to evaluate FFAM. In outdoor point cloud scenes, objects are relatively small compared global scenes, so only operate points within twice the diagonal length of an object's ''s bounding box from its center. We use IoU between a prediction and ground truth as the measure score. Average IoU curves presented in Figure 5 aa-b), and Table 2 reports the area under the curve (AUC) for different categories. A lower Deletion AUC indicates a steeper drop the loU score, reflecting more pronounced impact of removed salient points. Conversely, higher Insertion AUC suggests a larger increase in the loU score per step. indicating the significance added salient points. Our methods have the fastest performance drop and largest increase for Deletion and Insertion, showing points highlighted in our saliency maps have a greater effect on detector predictions than the other methods.
Visual Explanation Accuracy. VEA calculates the point-level intersection over union (IoU) between the ground truth masks and saliency maps, which are thresholded at various values. The results of VEA for different object categories can be found in Table [ Notably, our FFAM achieves the highest VEA scores across all categories, indicating the compactness of the visual explanations generated by FFAM. On the other hand, OccAM and Grad-CAn -rad exhibit lower performance on this metric. OccAM tends to mark significant number of background points, while Grad-CAM is class-specifer visual explanation method, which may explain their comparatively weak performance.
Pointing Game. To further assess the localization capability of FFAM, we present the results of the Pointing game (PG). In this evaluation, a hit is recorded if the point with the highest saliency value falls within the ground truth bounding box, while a miss is counted otherwise. The PG metric

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Figure 6: Average saliency maps for true and false positives. The 1 and 2nd rows represent cases of true and false positives, respectively.
PG
enPG
Method
All
Car
Ped.
Cyc.
All
Car
Ped.
Cyc.
Grad-CAM
0.093
0.080
0.166
0.163
0,021
0.022
0.014
0.011
ODAM
0.901
0.895
0.939
0,926
0.633
0.639
0.577
0.654
OccAM
0.946
0.957
0.898
0.860
0,023
0.024
0.019
0.013
FFAM (Ours)
0.991
0.989
0.999
0.998
0.664
0.671
0.591
0.719
Table 3: Comparison of Pointing game (PG) and energy-based Pointing game (enPG) metrics.
measures the accuracy of saliency maps by calculating the ratio of hits to the total number of hits and misses. Furthermore, we report the energy-based PG metric (enPG) proposed in [33], which considers the energy within the ground truth region compared to the global scene. As shown in Table 3 our FFAM surpasses previous methods on all metries, indicating its superior ability focus on the explained object. Notably, Grad-CAM performs poorly on both PG and enPG, which aligns with the VEA results presented in T This suggests that classification-brn are insufficient for generating meaningful explanations for detectors.
explanation methods alone
4.3 Modes of False Positive
FFAM can be used identify false positive modes of a detector. A detection is considered true positive if correctly classified and the Intersection over Union (IoU) between the prediction box and ground truth exceeds threshold. Otherwise, it is false positive. The IoU thresholds are 0.7, 0.5, and 0.5 for car, pedestrian, and cyclist objects, aligning with the KITTI official metric 77]. To reveal detection modes, we compute average saliency maps separately for true positives and false positives. Results are shown in Figure 6 First, we observe the average saliency maps of false positives exhibit similarities to those of true positives. The detector predicts a false positive because detects a similar pattern to that of true positive. Second, false positives tend to be surrounded by more noise points, with point density of approximately one-third of true positives. We believe noises and sparse density may be significant factors contributing to the occurrence of false positives. Lastly, the ratio of car, pedestrian, and cyclist objects in true positives is approximately 36:5:2, while in false positives, is 13:8:2. This suggests objects are less prone to false positives compared to pedestrian and cyclist objects.
5 Conclusion
In this paper, we propose a visual explanation method, FFAM, that efficiently generates high-quality explanations for 3D detectors. FFAM utilizes non-maximum matrix factorization to obtain a global concept activation map, which is then refined using object-specific gradients. To align the granu- larity of the input point cloud and intermediate features, we introduce voxel upsampling strategy. Qualitative and quantitative experiments demonstrate that our FFAM provides more interpretable and compact visual explanations than previous methods. The limitation of FFAM is that it needs to access the feature maps within 3D detectors. In future work, we will explore using visual explanations to enhance the accuracy and efficiency of 3D detectors.

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Acknowledgments
This work was supported by the Project of National Natural Science Foundation of China (No. 62232008) and Guangxi Key R & D Program (No. GuikeAB24010524,
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A Appendix
Layer
Del. 4
Ins. 1
VEA f
PGT
enPGi
conv1
0.167
0.911
0.084
0.885
0.562
conv2
0.102
0.912
0.116
0.905
0.615
conv3
0.091
0.909
0.161
0.955
0.654
conv4
0.093
0.905
0.208
0.946
0.644
Table 4: Results of different layer setrings. "convl', "conv2', 'conv3' and 'conv4' represent the 1st 2nd 3rd and 4th blocks in the 3D backhone.
(Range, k)
Del. +
Ins. 1
VEA f
PG f
enPG
(0, I)
0.091
0.909
0.161
0.955
0.654
(1, 4)
0.076
0.910
0.218
0.965
0.635
(2, 16)
0.069
0.909
0.313
0.981
0.644
(3, 64)
0.072
0.907
0.309
0.980
0.642
Table 5: Results of different (Range, k) settings. 'Range" means the Manhattan distance threshold
and & denotes the upper bound of neighbor number in the voxel upsampling strategy.
r
Del.
Ins. f
VEA 1
PG
enPG 1
8
0.067
0.909
0.315
0.980
0.639
16
0.069
0.909
0.313
0.981
0.644
32
0.069
0.909
0.313
0.981
0.647
64
0.071
0.907
0.391
0.991
0.664
128
0.069
0.909
0.306
0.980
0.644
Table 6: Results of different concept number settings. denotes the concept number.
A.1 Hyperparameters Analysis
In this section, we determine suitable hyperparameters including the feature map position, concept number r and sampling range for FFAM. Specifically, we select feature maps from the 1sr, 2nd 3rd and 4hh blocks of 3D backbone of SECOND, and then generate visual explanations utilizing these feature maps. As shown in Table selecting the feature maps from the 3rd block obtains the best results on most metrics. Therefore, in other experiments, we utilize the feature maps from the 3rd block. Then, to determine the setting of the sampling range, we set different combinations of the Manhattan distance threshold and neighbor number k. As illustrated Table 5 (2. 16) is an appropriate setting for FFAM. Finally, we set equal to 8, 16, 32, 64 and 128, respectively. The experimental results are shown in Table As we can see, r = 64 greatly outperforms other settings Consequently, we select r = 64 as the default setting.
on VEA, PG and enPG metrics, and performs slightly worse on Deletion and Insertion metrics.
A.2 Average Saliency Maps for Other Detectors
To reveal the detection modes of additional detectors, we present average saliency maps from various 3D detectors, namely CenterPoint [37], DCDet (12), PV-RCNN (11). and Voxel R-CNN [5].
CenterPoint and DCDet are trained and evaluated using Waymo Open 26 while PV-RCNN and
Voxel R-CNN employ KITTI 77) for training and evaluation. The results are displayed in Figure 7 Notably, for pedestrian and cyclist categories, different detectors trained on distinct datasets exhibit similar areas of focus, such as the head and shoulder regions for pedestrians and the head and back regions for cyclists. However, the car/vehicle category, detectors trained on diverse datasets reveal distinct patterns. Detectors trained Waymo Open tend to concentrate the front, back, and A- A-pillars of the vehicle category, whereas detectors trained on KITTI tend to emphasize the four corners of the car category.
12

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CenterPoint
DCDet
PV-RCNN
Voxel R-CNN
Figure 7: Average saliency maps for different detectors. Voxel R- R-CNN is only available for the car category (parameter weights are downloaded from OpenPCDet (30]
Deletion
J
Insertion
Method
All
Vehicle
Ped.
Cyc.
All
Vehicle
Ped.
Cyc.
Grad-CAM
0.332
0.380
0.239
0.133
0,805
0.833
0.777
0.804
ODAM
0.252
0,279
0.200
0,151
0.853
0.875
0.809
0,824
OccAM
0.562
0.594
0.505
0.357
0.871
0.893
0.826
0.832
FFAM (Ours)
0.095
0.120
0.085
0.104
0.897
0.922
0.845
0.853
Table AUC for Deletion and Insertion curves.
The results of different categories are reported. 'all'
means the combination of the three categories.
PG
enPG
Method
All
Car
Ped.
Cyc.
All
Car
Ped.
Cyc.
Grad-CAM
0.028
0.016
0.045
0.247
0.006
0.008
0.002
0.004
ODAM
0.917
0.941
0,865
0,970
0.476
0.568
0.285
0.575
OccAM
0.691
0.681
0.712
0.634
0.004
0.005
0.001
0.002
FFAM (Ours)
0.975
0.980
0.963
0.980
0.517
0.597
0.349
0.650
Table 8: Comparison of Pointing Game and energy-based based Pointing Game metrics.
VEA
f
Method
All
Car
Ped.
Cyc.
Grad-CAM
0.009
009
0.010
0.027
ODAM
0.234
0.220
0.261
0.260
OceAM
0.012
0.010
0.018
0.012
FFAM (Ours)
0.388
0.358
0.448
0.458
Table 9: Comparison of visual explanation accuracy metric for different categories. 'all' denotes the three categories are included.
13

---

0G
VU
FF
Del.
Ins. f
VEA
PG
enPG 1
0.082
0.909
0.252
0.933
0.567
0.076
0.906
0.335
0.957
0.563
0.071
0.907
0.391
0.991
0.664
Table 10: Effect of different components of FFAM. OG, VU and FF denote object-specific gradient, voxel upsampling and feature factorization, respectively.
A.3 Quantitative Results on Waymo Open Dataset
To further assess the visual explanation quality produced by our FFAM, we conducted experiments on Waymo Open [26], utilizing CenterPoint [7] as the detector for interpretation. We also employ the Deletion, Insertion, PG, enPG and VEA as the evaluation metrics. The results, presented in Table showcase the superior performance of our FFAM across all metrics, mirroring the outcomes observed on KITTI. These findings demonstrate FFAM's remarkable adaptability to diverse detectors trained on different datasets.
A.4 Ablation Study
To investigate the impact each component FFAM, we conduct an ablation analysis using the SECOND detector on KITTI. Initially, utilize the object-specific gradient alone to generate saliency maps, which yield relatively satisfactory results, as depicted in the first row Table [0] Subsequently, introduce the voxel upsampling strategy into the flow, resulting in significant improvements across most metrics, notably VEA and PG, as indicated in the second row of Table TO Lastly, incorporate the complete FFAM components. The third row of Table 10 demonstrates that feature factorization greatly enhances the quality of saliency maps, with the PG metric approaching a value close to 1, signifying the precise localization achieved by FFAM. We also observe that voxel upsampling and feature factorization do not yield improvements for the Insertion metric. We believe this is due to the models relying, some extent, the neighbor context for detection, whereas voxel upsampling and feature factorization result in a more compact saliency map (i.e., focusing on the object itself).
14

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Reviews:
[{"review_id": "SNFpeQiKHC", "reviewer": "Reviewer_93Lo", "summary": "The paper proposes a “FFAM” for visual visualization of 3D Detectors. It introduces a non-negative matrix factorization (NMF) to decomposing 3D features into the product of two non-negative matrices. Besides, an object-specific loss is utilized to generate the object-specific saliency maps. Finally, the voxel upsampling is used to recover the resolution of the activation maps.", "questions": "Please the weakness.", "limitations": "The authors mentioned the limitations and societal impact in the checklist.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This work introduces NMF in explaining point cloud detectors and utilizes feature gradients of an object-specific loss to generate object-specific saliency maps.\n2. A voxel upsampling strategy is proposed to upsample sparse voxels.", "weaknesses": "1. The description of Non-negative Matrix Factorization (NMF) is unclear. When the point cloud is large in scale, NMF may become unstable and exhibit a long convergence time.\n2. The core innovation of this paper lies in feature factorization. The authors directly chose NMF but did not provide a reason for this choice. For instance, Principal Component Analysis (PCA) can also achieve feature factorization.\n3. The object-specific gradient is element-wise multiplied with the global concept activation map to obtain the specific activation map of the object. In other words, the object-specific gradient inhibits the activation that do not belong to the current object. Therefore, it seems reasonable to directly use the weighting of the object-specific gradient as the activation maps.\n4. Since the gradient map G can already represent the current object, why not perform NMF on the gradient map and then use a method similar to obtaining a global activation map to derive the object activation map?\n5. Object-Specific Gradient Weighting is a general module. Can it be applied to other methods of generating activation maps, such as OccAM?\n6. Regarding voxel upsampling, the authors provide a limited description. I am curious about why they chose the Gaussian kernel and how it compares to other upsampling methods such as trilinear interpolation and transpose convolution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.\n\n**Q1.** The description of Non-negative Matrix Factorization (NMF) is unclear. When the point cloud is large in scale, NMF may become unstable and exhibit a long convergence time.\n\n**Authors’ Reply:** \nIn our experiments, Non-negative Matrix Factorization (NMF) maintains excellent stability and rapid convergence times across different scales of point cloud, encompassing those from the KITTI and Waymo Open datasets. The implementation of NMF is facilitated by well-established libraries, which can be seamlessly integrated with PyTorch code.\n\n**Q2.** The core innovation of this paper lies in feature factorization. The authors directly chose NMF but did not provide a reason for this choice. For instance, Principal Component Analysis (PCA) can also achieve feature factorization.\n\n**Authors’ Reply:** \nWe agree with your perspective that Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are both effective tools for feature decomposition and dimensionality reduction. However, the basis vectors derived from PCA can not represent a clear semantic concept, and thus are primarily used for data dimensionality reduction. In contrast, the basis vectors of NMF exhibit non-negativity and additivity, often representing specific concepts such as car doors and wheels. The DFF [1] is the first work that employs NMF to localize semantic concepts within images. Inspired by DFF, we utilize NMF to reveal the latent semantic concepts embedded in the intermediate voxel features of 3D object detectors. Typically, voxel features that provide effective cues for detection in 3D detectors possess richer semantic concepts, which can be used to create saliency maps.\n\n**Q3.** The object-specific gradient is element-wise multiplied with the global concept activation map to obtain the specific activation map of the object. In other words, the object-specific gradient inhibits the activation that do not belong to the current object. Therefore, it seems reasonable to directly use the weighting of the object-specific gradient as the activation maps.\n\n**Authors’ Reply:**\nWe agree with your point that object-specific gradient can serve as the activation map. However, it only highlights the object-specific region in a point cloud and is struggle to differentiate the importance of point within the object-specific region. NMF can be used to help uncover the recognition pattern within the object-specific region. As shown in Figure 4, NMF assists in identifying the detector's distinct recognition patterns for various categories and object attributes. The quantitative results in Table 9 emphasize the enhanced performance achieved by integrating the object-specific gradient with NMF. This confirms the significant role of NMF in obtaining more nuanced and detailed visual explanations.\n\n**Q4.** Since the gradient map G can already represent the current object, why not perform NMF on the gradient map and then use a method similar to obtaining a global activation map to derive the object activation map?\n\n**Authors’ Reply:**\nThe raw point features contain substantial semantics which is beneficial for NMF to uncover latent concepts within these features. However, the gradient map G only contains backward gradients for a specific object. Consequently, employing NMF to extract concepts from gradient maps lacks a clear conceptual basis.\n\n**Q5.** Object-Specific Gradient Weighting is a general module. Can it be applied to other methods of generating activation maps, such as OccAM?\n\n**Authors’ Reply:**\nwe believe that incorporating object-specific gradient weighting into other methods is feasible. But we haven't tried combining the weighting module with other methods yet. Because other methods usually have their own approaches of obtaining object-level visual explanations. For example, OccAM is a perturbation-based method that involves randomly masking the input point cloud to assess performance changes in the output. As it inherently serves as an object-level explanation method, there is no immediate necessity to integrate it with object-specific gradient weighting. Consequently, we have not attempted to combine these techniques.\n\n**Q6.** Regarding voxel upsampling, the authors provide a limited description. I am curious about why they chose the Gaussian kernel and how it compares to other upsampling methods such as trilinear interpolation and transpose convolution.\n\n**Authors’ Reply:**\nGiven that voxels are sparsely scattered throughout 3D space, identifying all neighboring points for a given point to be interpolated presents a challenge. Consequently, trilinear interpolation is not well-suited for handling sparse voxels. On the other hand, transpose convolution, being a learning-based technique, does not align as the optimal choice within our framework. In contrast, our voxel upsampling method initiates by searching for neighbors within a specified range and subsequently applies a Gaussian kernel to weigh them. This method is more adaptable for our FFAM.\n\n[1] Edo Collins, Radhakrishna Achanta, and Sabine Susstrunk. Deep feature factorization for concept discovery. In ECCV, 2018.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "scKa6uaGEq", "reviewer": "Reviewer_bytz", "summary": "This paper addresses the challenge of explanation and interpretability in 3D detection methods. It introduces a Feature Factorization Activation Map (FFAM), which utilizes non-negative matrix factorization (NMF) and object-specific gradient weighting to generate global and object-specific activation maps at the voxel level. An up-sampling method is subsequently employed to produce per-point activation maps. Extensive quantitative and qualitative experiments demonstrate the effectiveness of the proposed FFAM in generating saliency maps for various points over the previous method.", "questions": "Please check weaknesses.", "limitations": "It would strengthen the paper if an example could be provided demonstrating how FFAM can help identify some non-trivial error modes and offer insights for improving the detector.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The topic is interesting and warrants investigation. As discussed in Section 4.3, the research can significantly enhance 3D object detectors, particularly in identifying false positive modes.\n- The quantitative results are convincing and relevant to the topic.\n- Overall, the paper is well-written and easy to comprehend.\n- The code is made available, promoting transparency and reproducibility.\n- The paper discusses the limitation of the proposed method, specifically the necessity of accessing the feature map.", "weaknesses": "- The rationale for using NMF is unclear. It appears to be a learning-based, PCA-like method to extract saliency from voxel features. How does it compare to the proposed method on the global activation map?\n- There is a lack of ablation studies, such as those examining the parameter  𝛾\n- The gradient weighting method is intriguing and seems sensible. Is this method original to your work?\n- It would be beneficial to use 𝑊 as a weight in Equation 2. The current version is a little confusing. \n- In Section 4.1, it is claimed that the saliency map generated by FFAM is superior to occAM because it is more focused on the object. Please provide further validation on why a clearer activation map is considered better.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.\n\n**Q1.** The rationale for using NMF is unclear. It appears to be a learning-based, PCA-like method to extract saliency from voxel features. How does it compare to the proposed method on the global activation map?\n\n**Authors’ Reply:** \nNon negative matrix factorization (NMF) is a matrix factorization technique used to discover potential concepts in features. The process of NMF is to approximately decompose a non negative matrix $V$ into the product of two non negative matrices H and W, i.e. $V ≈ HW$. Among them, $W$ and $H$ are non negative, which makes NMF more interpretable in many fields. For example, in facial recognition, the basis vectors in the $W$ matrix usually represent specific concepts such as nose, eyes, mouth, etc [1]. In our work, we utilize NMF to uncover latent concept within voxel features of 3D detectors. Typically, voxel features with effective detection clues in 3D detectors contain richer semantic concepts. Therefore, we could sum the activation coefficients of different concepts in $H$ matrix to obtain a global activation map. The basis vectors obtained by PCA do not have clear semantic concepts. Therefore, we have not compare our method with PCA-like method.\n\n**Q2.** There is a lack of ablation studies, such as those examining the parameter $r$.\n\n**Authors’ Reply:**\nDue to page limitations in the main body of the paper, we have included the hyperparameter analysis and ablation experiments in the appendix. Please refer to Appendix A.1 and A.4 for details.\n\n**Q3.** The gradient weighting method is intriguing and seems sensible. Is this method original to your work?\n\n**Authors’ Reply:**\nAs far as we know, we are the first one that use the gradient to refine a global activation map. The previous method that is closest to our method is ODAM. It also utilizes backward gradients to generate saliency maps. However, there are two main difference between our method and ODAM. Firstly, the usage of gradients is different. ODAM multiply the gradient map with mid feature maps, and then sum the values along the channel dimension to obtain the final saliency maps. While our FFAM utilizes the gradients to generate a weighting item as follows:\n$$\n\\omega = \\sum_{k=1}^{d} \\left| G_{\\cdot k} \\right|,\n$$\nwhere $G_{\\cdot k}$ refers to the $k$-th channel of gradient map $G$, and $d$ denotes the number of channels. Secondly, ODAM is designed to generate visual explanations for image detectors. While our FFAM is used to explain LiDAR-based 3D detectors.\n\n**Q4.** It would be beneficial to use $W$ as a weight in Equation 2. The current version is a little confusing.\n\n**Authors’ Reply:**\nEquation 2 represents the process of solving for NMF. Since it is difficult to find a numerical solution for the non-negative matrix factorization of matrix $A$, only an approximate solution can be obtained. We attempt to find a matrix $\\hat{A}$ that closely approximates $A$, while also satisfying the product of two non-negative matrices $H$ and $W$.\n\n**Q5.** In Section 4.1, it is claimed that the saliency map generated by FFAM is superior to OccAM because it is more focused on the object. Please provide further validation on why a clearer activation map is considered better.\n\n**Authors’ Reply:**\nIn the context of visual explanation for 3D detectors, a clearer activation map is considered superior because it aids in better identifying the region of interest within the point cloud. We have conducted quantitative experiments to validate our method and previous explanation methods. As shown in Table 1 and Table 3, our FFAM achieves best results on VEA, PG and enPG metrics. These metrics reflect the degree of focus of an explanation method on an object. Furthermore, our FFAM performs best on the Deletion and Insertion metrics which are are widely used to evaluate explanation methods. As shown in Figure 5, our methods have the fastest performance drop and largest increase for Deletion and Insertion respectively, showing points highlighted in our saliency maps have a greater effect on detector predictions than the other methods.\n\n**Q6.** It would strengthen the paper if an example could be provided demonstrating how FFAM can help identify some non-trivial error modes and offer insights for improving the detector.\n\n**Authors’ Reply:**\nIn section 4.3, we utilize FFAM to find the modes of false positives generated by a detector. There are three observations in the experiments. We believe these observations will provide some insights for researchers to improve 3D detectors. First, we observe the average saliency maps of false positives exhibit similar similarities to those of true positives. The detector predicts a false positive because it detects a similar pattern to that of a true positive. Second, false positives tend to be surrounded by more noise points, with a point density of approximately one-third of true positives. We believe noises and sparse density may be significant factors contributing to the occurrence of false positives. Lastly, the ratio of car, pedestrian, and cyclist objects in true positives is approximately 36:5:2, while in false positives, it is 13:8:2. This suggests car objects are less prone to false positives compared to pedestrian and cyclist objects.\n\n[1] Daniel D Lee and H Sebastian Seung. Learning the parts of objects by non-negative matrix factorization. nature, 1999.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hxV1Pv5A52", "reviewer": "Reviewer_v6wE", "summary": "The paper proposes a method called Feature Factorization Activation Map (FFAM) to provide visual explanations for 3D object detectors based on LiDAR data. This method addresses the interpretability issue in 3D detectors by using non-negative matrix factorization to generate concept activation maps and refining these maps using object-specific gradients. The approach is designed to handle the unique challenges of 3D point cloud data, such as sparsity and the need for object-specific saliency maps. The paper evaluates FFAM against existing methods and demonstrates its effectiveness through qualitative and quantitative experiments.", "questions": "1. Real-Time Applicability: How does the computational overhead of FFAM compare to existing methods in real-time applications, especially in autonomous driving scenarios?\n2. How sensitive is FFAM to the choice of detector? We noticed that two kinds of detectors were used in this paper. More comparison and analysis are necessary.\n3. Can FFAM be extended to other 3D detectors or modalities, such as RGB-D sensors or radar data? If so, what modifications would be necessary?", "limitations": "1. FFAM requires access to the feature maps within 3D detectors, which may not always be possible, especially for proprietary or closed-source systems.\n2. The method's scalability to large-scale or real-time applications is not fully addressed. The computational requirements may be prohibitive for some practical applications.\n3. The method is tailored to LiDAR data and may not directly translate to other types of 3D data without significant modifications.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Using non-negative matrix factorization to generate concept activation maps is novel and well-justified for the application.\n2. The method is evaluated on multiple datasets and compared against state-of-the-art methods, demonstrating its superiority in producing high-quality visual explanations.\n3. The paper provides a clear and detailed description of the methodology, including the feature factorization, gradient weighting, and voxel upsampling processes.\n4. The proposed method has practical implications for improving the interpretability of 3D object detectors, which is crucial for applications in autonomous driving and robotics.", "weaknesses": "1. The method involves several computationally intensive steps, such as non-negative matrix factorization and voxel upsampling, which may limit its applicability in real-time systems.\n2. The paper focuses primarily on LiDAR-based 3D detectors. Discussing how the method could be adapted or extended to other types of 3D data or detection systems would be beneficial.\n3. While the evaluation is comprehensive, it primarily focuses on two datasets (KITTI and Waymo Open). Additional datasets and scenarios could further validate the robustness and generalizability of the method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.\n\n**Q1.** Real-Time Applicability: How does the computational overhead of FFAM compare to existing methods in real-time applications, especially in autonomous driving scenarios?\n\n**Authors’ Reply:**\nCompared to the existing method OccAM, our approach has a significant advantage in speed. NMF and voxel upsampling are implemented by CUDA operators which could use GPU to accelerate the process. The main overhead of our FFAM lies in gradient backpropagation. Therefore, the delay of FFAM depends on the size of the model being explained. Currently, the backpropagation delay of most 3D object detectors is around 100ms. On the other hand, the existing method OccAM is a perturbation-based explanation method, which requires extensive sampling of inputs and then processing them one by one. As a result, this process is very slow. Typically, using OccAM to obtain an visual explanation requires several minutes. However, visual explanation methods (including our FFAM and OccAM) belong to post hoc explanations of the model. They are not used in real-time applications of autonomous driving. Therefore, we did not compare the speed of these methods in the paper.\n\n**Q2.** How sensitive is FFAM to the choice of detector? We noticed that two kinds of detectors were used in this paper. More comparison and analysis are necessary.\n\n**Authors’ Reply:**\nFFAM is not specific to detector architecture. It can be widely applied to existing LiDAR-based 3D detectors. In Appendix A.2, we apply FFAM to some other state-of-the-art detectors, such as DCDet, PV-RCNN, and Voxel R-CNN.\n\n**Q3.** Can FFAM be extended to other 3D detectors or modalities, such as RGB-D sensors or radar data? If so, what modifications would be necessary?\n\n**Authors’ Reply:**\nFFAM can be extended to extensive 3D detectors. In this paper, our main focus is on studying point clouds as the input format. FFAM has not yet been applied to other modalities. But we believe that RGB-D and radar data can be converted into point cloud format, so these modalities should also be feasible for FFAM to process.\n\n**Q4.** FFAM requires access to the feature maps within 3D detectors, which may not always be possible, especially for proprietary or closed-source systems.\n\n**Authors’ Reply:**\nWe concur with your observation regarding the limitation in FFAM. We also pointed out this limitation of FFAM in the conclusion section. We believe that FFAM can primarily be used to provide researchers with some insights to improve detectors. In addition, it can also be used to reveal the internal working mechanism of detectors to users, thereby improving their understanding and trust in detectors.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kp3205dLwo", "reviewer": "Reviewer_BgKE", "summary": "This paper proposes a feature factorization activation map to explain 3D detectors. It uses non-maximum matrix factorization (NMF) to obtain a global concept activation map and then refine it with feature gradients of an object-specific loss. A voxel upsampling strategy is further proposed to upsample sparse voxels to align the granularity between the activation map and input point cloud. Both quantitative and qualitative results validate the efficacy of the proposed method.", "questions": "None.", "limitations": "None.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The basic idea is easy to follow, and the comparison with previous works (both in 2D and 3D) is clear.\n- The key motivation and contributions are clearly presented.\n- The content of methodology and implementation are well organized.\n- The visualization results are impressive. Both the quantitative and qualitative results convincingly support the efficacy of the proposed explanation method.", "weaknesses": "The only concern is about the application, or the value in applications, of this proposed method. I understand this is a good adaptation and attempt to apply such explanation methods on LiDAR-based 3D detectors, but it is still unclear how it can benefit the procedure of improving 3D detectors or downstream applications. I would be more interested in how it produces a stronger 3D detector or a 3D detector with better controllability and safety, especially for safety-critical scenarios like autonomous driving.\n\nIn addition, there are also related areas or applications, such as 3D detection in indoor scenes (ScanNet, SUN RGB-D), etc, and camera-based/multi-modality 3D detection. I am also curious about the performance of such explanation methods when applied to those problems. How about the performance, and is there any new challenge?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your insightful feedback. Below please find our clarifications in response to your comments.\n\n**Q1.** The only concern is about the application, or the value in applications, of this proposed method. I understand this is a good adaptation and attempt to apply such explanation methods on LiDAR-based 3D detectors, but it is still unclear how it can benefit the procedure of improving 3D detectors or downstream applications. I would be more interested in how it produces a stronger 3D detector or a 3D detector with better controllability and safety, especially for safety-critical scenarios like autonomous driving.\n\n**Authors’ Reply:**\nWe fully agree with your viewpoint. Our FFAM can be used to find the regions of interest of detectors on point cloud input. It also can provide visual explanation for different object attributes at a fine-grained level. In section 4.3, we utilize FFAM to reveal the detection mode of false positive predictions generated by detectors. There are three main observations in the experiment. First, we observe the average saliency maps of false positives exhibit similarities to those of true positives. The detector predicts a false positive because it detects a similar pattern to that of a true positive. Second, false positives tend to be surrounded by more noise points, with a point density of approximately one-third of true positives. We believe noises and sparse density may be significant factors contributing to the occurrence of false positives. Lastly, the ratio of car, pedestrian, and cyclist objects in true positives is approximately 36:5:2, while in false positives, it is 13:8:2. This suggests car objects are less prone to false positives compared to pedestrian and cyclist objects. We think that the above observations can help researchers to design more efficient and reliable 3D object detectors.\nWe believe there are more applications of FFAM that can be explored. In our future work, we will use FFAM to improve the performance (including accuracy and speed) of 3D detectors.\n\n**Q2.** In addition, there are also related areas or applications, such as 3D detection in indoor scenes (ScanNet, SUN RGB-D), etc, and camera-based/multi-modality 3D detection. I am also curious about the performance of such explanation methods when applied to those problems. How about the performance, and is there any new challenge?\n\n**Authors’ Reply:**\nIn this paper, our main focus is on explaining LiDAR-based 3D detectors on outdoor scenes. For indoor scenes or other modalities as input, as long as the intermediate features of the network can be directly linked to the input, our FFAM can be applied effectively. We believe that the most challenging task would be explaining 3D detectors based on multi-view cameras. This is because the correspondence between the intermediate features of such detectors and the input is relatively intricate. Many of them employ learning-based methods to elevate two-dimensional images into three-dimensional space.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents a method for analyzing LiDAR-based 3D detectors. The main idea is to generate pointcloud-based and voxel-based saliency maps, revealing which points contribute most to a given 3D detection. These saliencies can also be broken down into contributions toward specific attributes, such as the individual parameters of a box. Before and after the rebuttal phase, reviews were borderline, but a solid “borderline accept” consensus emerged (with all reviewers giving this rating). Reviewer BgKE is unsure on how the analysis will be used to improve detection algorithms, but also notes that the saliency visualizations are “impressive”. Reviewers 93Lo and bytz raised some technical concerns, which appear to have been diligently resolved by the authors. Reviewer v6wE pointed out some limitations of the work, which the authors might work to clarify in the text, but these are largely clear already. Given the consensus positive reviews, the good results (quantitative and qualitative), and the improvements and clarifications provided in rebuttal, the AC recommends acceptance. The authors are encouraged to carefully revise the paper for camera-ready, especially to include the technical clarifications worked out during the rebuttal phase.
Decision: Accept (poster)