Event-Driven Dynamic Scene Depth Completion
Authors: Zhiqiang Yan
Jianhao Jiao
Zhengxue Wang
Gim Hee Lee
Venue: NeurIPS 2025 poster ()
Keywords: depth completion
event guidance
dynamic scene
Primary area: applications

Abstract: Depth completion in dynamic scenes poses significant challenges due to rapid ego-motion and object motion, which can severely degrade the quality of input modalities such as RGB images and LiDAR measurements. Conventional RGB-D sensors often struggle to align precisely and capture reliable depth under such conditions. In contrast, event cameras with their high temporal resolution and
sensitivity to motion at the pixel level provide complementary cues that are beneficial in dynamic environments. To this end, we propose EventDC, the first event-driven depth completion framework. It consists of two key components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). Both modules adaptively learn the two fundamental components of convolution operations: offsets and weights conditioned on motion-sensitive event streams. In the encoder, EMA leverages events to modulate the sampling positions of RGB-D features to achieve pixel redistribution for improved alignment and fusion. In the decoder, LDF refines depth estimations around moving objects by learning motion-aware masks from events. Additionally, EventDC incorporates two loss terms to further benefit global alignment and enhance local depth recovery. Moreover, we establish the first benchmark for event-based depth completion comprising one real-world and two synthetic datasets to facilitate future research. Extensive experiments on this benchmark demonstrate the superiority of our EventDC. [Project page](https://yanzq95.github.io/projectpage/EventDC/index.html).

Event-Driven Dynamic Scene Depth Completion
Zhiqiang Yan'
Jianhao Jiao Zhengxue Wang? Gim Hee Lee'
National University Singapore University College London *Nanjing University of Science and Technology Cyanzq, gimhee. 1ee) eunus @nussedu.ss.
Abstract
Depth completion in dynamic scenes poses significant challenges due to rapid ego-motion and object motion, which can severely degrade the quality of input modalities such as RGB images and LiDAR measurements. Conventional RGB-D sensors often struggle align precisely and capture reliable depth under such conditions. contrast, event cameras with their high temporal resolution and sensitivity to motion at the pixel level provide complementary cues that are ben- eficial dynamic environments. To this end, we propose EventDC, the first event-driven depth completion framework. It consists of two key components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). Both mod- ules adaptively learn the two fundamental components of convolution operations: offsets and weights conditioned on seniiin-eenitive event streams. In the encoder, EMA leverages events modulate the sampling positions of RGB-D features achieve pixel redistribution for improved alignment and fusion. In the decoder, LDF refines depth estimations around moving objects by learning motion-aware masks from events. Additionally, EventDC incorporates two loss terms to further benefit global alignment and enhance local depth recovery. Moreover, we establish the first benchmark for event-based depth completion comprising one real-world and two synthetic datasets to facilitate future research. Extensive experiments on this benchmark demonstrate the superiority of our EventDC. Project page
Introduction
Depth completion
48
36
47
aims to predict dense depth from sparse measurements,
typically using auxiliary modalities such as RGB images. As a cornerstone of 3D perception, it plays a crucial role in a wide range of downstream applications including self-driving [70]
57 26 [5]]
augmented reality 45
etc. Although recent methods have demonstrated impressive results in static scenes, dynamic environments remain highly challenging. As illustrated in Fig. Ta), the rapid ego-motion results in blurry RGB images and misalignment with LiDAR measurements, while fast-moving objects further exacerbate depth inaccuracies in their vicinity. These challenges make precise depth completion even more difficult.
60 54
66
71). scene understanding [56] 40 78
53
72
The unique characteristies of event cameras [T0 ventional RGB-D sensors in dynamic scenes. Their microsecond-level temporal resolution enables the reliable capture of rapid ego-motion without introducing motion blur, and their asynchronous change-driven operation makes them inherently well-suited for detecting fast-moving objects. These properties help mitigate the limitations of RGB-D measurements by offering temporally consistent and low-latency signals, particularly in regions where traditional sensors often fail. As a result, event- based sensing proves especially advantageous for depth completion in highly dynamic environments.
provide a compelling complement to con-
In this work, present EventDC, novel depth completion framework that leverages event data to tackle the challenges posed by dynamic scenes, As shown in Fig. [bb,, the core idea to exploit the unique properties of event streams to guide depth completion especially in motion- affected regions.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

RGB
seanthyy
Microsecond-leve motion cues
reselation
LiDAR
emoode
RGB-D
decoder
Local depth refinement
Eventt
alignment
(a) Challenging dynamic scene
(b) Our event-driven solution
Figure 1: Data example and our solution for depth completion in dynamic environments, Leveraging high temporal resolution and motion sensitivity, event provides valuable complementary information for depth completion in dynamic scenes. Multiple event streams aggregated for clear visualization.
To this end, our EventDC incorporates two key components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). EMA is encoder-side module that adaptively adjusts convolutional sampling positions using event information to achieve pixel redistribution for enhanced global aligmment and more effective molti-modal fusion between RGB and LiDAR features. Furthermore, incorporates structure-awan loss to mitigate the RGB-D inconsistency caused by rapid ego-motion. LDF decoder-side module that focuses on refining depth around moving objects. It first learns motion masks from event streams to get the regions influenced by object motion. The learned masks are then used by LDF with local motion-awar constraint to facilitate more accurate depth predictions in these regions. Concurrently, the two modules enable our EventDC to address both global misalignment and local depth inaccuracies for handling complex scenarios involving motion.
Additionally, depth completion based on event cameras remains an underexplored area with no existing event-based depth completion datasets date. To address this gap, introduce the first event-based depth completion benchmark, which includes real-world dataset SeanDD--Redl semi-synthetic dataset EventDC-SemiSyn, and fully synthetic dataset EvemDDC-FullSy.
In summary, our contributions are as follows:
To the best of our knowledge, we are the first to introduce EventDC, novel event-driven depth completion framework designed address the challenges dynamic environments.
We present two event-driven modules: EMA and LDF which are designed to mitigate the global misalignment caused by ego-motion and local depth inaccuracies due to object motion. Additionally, these two modules are jointly supported by two dedicated loss constraints.
To foster further research, we build the first event- bastd benchmark for depth completion. Extensive experiments across these datasets demonstrate the superiority of our approach, with up to 12.8% improvement on the best-performing dataset over suboptimal methods.
2
Related Work
Depth Completion. Early depth completion methods 48
49 32 focus predicting dense 33 25
depth maps directly from sparse inputs. For example, IP-Basic 25 uses traditional image processing techniques densify sparse depth without deep learning. In contrast, Uhrig al. 48 introduce Sparsity Invariant CÑNs, which adapt convolutional operations to varying input densities to ensure consistent performance. S2D 33 employs an encoder-decoder architecture to progressively densify sparse depth input. FusionNet (49 integrates global context and local structures with a confidence- driven refinement mechanism. Eldesokey et al. 77 present a confidence propagation method within CNNs to improve sparse depth regression by modeling uncertainty. Guided depth completion using [73
63, 64). Dynamic filtering techniques [6] 61, (2) generate adaptive filtering kemnlss from color images for effective extraction of depth features. Methods such as FuseNet [1) PointDC (67, BEVDC [7],, and TPVD (65)
color images has gained significant traction 46 70 65
20
69
47
further enhance depth completion by incorporating raw point clouds. Moreover, priors of the depth foundation models are used to improve generalization 33 37 51 16]. Recently, SigNet (66) redefines depth completion enhancement, densifying sparse depth with non-CNN methods, and then refines

---

it through a Degradation-awal aware framework. In addition, SPN techniques which serve as effective refinement modules, can further enhance performance.
B 4
36
59
20
28
65
Event-Based Depth Estimation. Depth estimation with event cameras p 15
30 58
attracts growing interest due the high temporal resolution, dynamic range, and low latency of asynchronous vision sensors, Early methods reconstruct depth solely from event streams such as the end-to-end framework by Hidalgo-Carrió et al. [17. the multi-view stereo pipeline EMVS [39], and unsupervised learning approaches for depth and egomotion 774]. DERD-Net [8] further exploits 3D convolutions and recurrence on event- based disparity space images, and Zhu et al. 777 propose a self- supervised framework for joint depth and optical flow estimation. Recent works leverage additional modalities to enhance event-based depth estimation. E.ooDpphh [75] temporally aligns
events and intensity frames for self-super monocular depth learning. Muglikar et al. [34 propose event-guided illumination control for active depth sensors. SRFNet (355 fuses frame and event features for fine-grained depth prediction with improved structure in both daytime and nighttime scenes. SDT 68 combines spiking neural networks and transformers for efficient depth estimation. Furthermore, contrast maximization that emerges fundamental principle for event-based motion, depth, and optical flow estimation 41 has inspired many subsequent works.
Dynamic Convolution. Dynamic convolution is method that adjusts the convolution operation based on input features, and gains significant attention in computer vision tasks. Techniques such as graph convolution and deformable convolution serve as specific manifestations of dynamic convolution. For example, ACMNet and GraphCSPN build graph structures enable effective multi- modal fusion and refinement. STN [2 introduces the concept of spatially transforming features within a network, although training such a mechanism
challenging task. Following this, DFN 23 proposes an approach that adapts filter parameters based on input features despite maintaining fixed kernel sizes. Deformable Convolution [5] 76 takes a different approach with the focus on dynamically adjusting sampling locations by generating offsets based on the geometric properties of objects. Similarly, Active Convolution (2) improves sampling by adjusting the locations while keeping the kernel shape fixed. More recently, GuideNet [46] develops a guided convolution block specifically designed for multi-modal data. Despite these innovations, dynamic mechanisms often add considerable complexity. To address this issue, RigNet [61] simplifies the dynamic guidance process by employing convolution factorization combined with attention [9]
3
Our Method
3.1
Background
The core of dynamic convolution lies in the adaptive determination of sampling positions and weights. Graph Convolutional Networks (GCNs) [24] and Deformable Convolutional Networks (DCNs) 76 serve as representative implementations of this concept. GCNs define sampling locations as neighboring nodes within the graph structure and compute adaptive weights during the aggregation stage. On the other hand, DCNs determine sampling locations through learned offsets and obtain adaptive weights by modulating predefined kernel weights with learned scalars. Both GCNs and DCNs can be viewed as extensions of standard convolutional operations, where the sampling locations and weights are made learnable and structure-aware. We use DCNs as an example to illustrate this dynamic learning process.
Specifically, DCNv1 (5] introduces learnable offsets for each sampling location to shift adaptively. Subsequently, DCNv2 (76) further incorporates a learnable modulation scalar for each sampling position that enables the assignment of varying importance to different locations. Given an input feature map x and a convolutional kernel with K sampling positions, let wy and P3 denote the weight and the pre-defined offset of the k-th position, respectively. DCNv2 can be formulated as:
K x((o) D Wk x(Po + P Apk) Amg.
(1)
where Po denotes the reference location, and Ap: and Am; are the learnable offset and modulation scalar, respectively. Note that wa and Am; can be jointly interpreted as unified learnable term. As
a result, the adaptive adjustment of offset and weight DCNv2 provides foundation for leveraging event data to tackle the challenges posed by fast motion in depth completion.
3

---

1/4
Event Modulated Alignment
1/4
1/8
Q
Color imagee
1/2
Dane depth D
Sparse depth
Local Depth Filtering (LDF)
D
10g
14
NEW
Event stream
Figure 2: Pipeline of our EventDC. The color image I, sparse depth and event stream are first processed by three structurally identical encoders. At each stage, the Event-Modulad Aligmment (EMA) block leverages event features to align and fuse RGB-D representations. In the decoder, the Local Depth Filtering (LDF) unit further enhances depth estimation around moving objects, guided by the inherent sensitivity of events to motion and reinforced by local motion-aware constraints.
3.2 EventDC Architecture
Overview. In highly dynamic environments, the proposed approach is designed to alleviate the adverse effects of fast motion that include global misalignment and local depth inaccuracies caused by ego-motion and object motion, respectively. Fig. illustrates the pipeline of our EventDC, which begins by employing three structurally consistent encoders to extract features from the color image sparse depth S, and event data E. This yields multi-scale representations {I1 I2, 13 La). {S1, S2, S3. }. and {E1, Ez, Eg. E,} at the (1/1, /2, /a. /s} stages, respectively. In the decoder, three deconvolution layers are applied to progressively generate (D3, D2. D1 at the f (1 /2. /13 stages. respectively. Furthermore, EventDC incorporates two key components: Alignment (EMA) and Local Depth Filtering (LDF). At each encoder stage, EMA predicts spatial offsets from event features and uses them to adjust the pixel distributions of RGB and depth features. This enables more precise multi-moda alignment and fusion. addition, a structure-aware loss introduced to further enhance the consistency. At the decoder stage, LDF leverages event features to estimate motion masks that identify moving objects. then refines the depth values within these regions using dynamic convolutions and local motion-aware loss, ultimately enhancing depth accuracy around the moving objects.
Event-Modulated Alignment. As depicted in Fig. at the j-th (j E {1, 2,44) stage of the three encoders, the EMA module takes as input the color image feature I,. sparse depth feature Sy. and event feature E,, each with dimensions xxx where C, H, and W denote the channel, height, and width, respectively. These inputs are first individually processed by three separate 3 x 3 convolutional layers Fr,, (-), Frya(1 (-) and Frza (.), with a stride of and output channels C, 2C, and C, respectively. The transformed event feature is then fused with the transformed RGB and depth features, respectively, resulting in the intermediate features:
Q, Fr, (I,) a..
(E,))
(2a)
Q F (s,) 3 (Fr (E,)),
(2b)
where F.(.) denotes the operation that splits the 2C--channel feature into two C-channel parts. a and B are learnable terms that control the contribution of the event term.
Subsequently, these two intermediate features are used to predict the offsets via two additional 3 x convolutions, Frza (.) and Frye (-), producing 2 H x W offsets and K x H x W weights:
Ap, W Fr,((())) (Q,),
(3a)
Ap,. W Frys (2,).
(3b)
'Implemented using torch.m. Parameter (zeros (1)) with the zero initialization designed to facilitate the progressive learning of event priors during training.

---

This step enables the model to adaptively determine the sampling locations by learning prior from event data that is sensitive fast motion. Consequently. the dynamic convolution in Eq. (T] can be used to perform pixel-wise adjustment of image and depth features formulated as follows:
I = F (I Apj. W,),
(4a)
F (Sy; Apj, Wy),
(4b)
where F-(-) denotes the generalized form of the operation defined in Eq. ( Note that Eq.44) emphasizes RGB-D pixel redistribution under highly dynamic conditions with offsets instead of adaptive weights. Consequently, in contrast to [.. weights with Am being the identity matrix. Subsequently, the redistributed RGB-D features further processed by a 3 x 3 convolution Fry0 (.) to obtain the fused feature F, E RCCH*W which formulated as:
[. both w, and w, correspond to the predefined
Fj F (I,+ S,).
(5)
Additionally, structure- loss Litt is introduced to enhance the consistency. Let G(.) denote sequence of single-channel convolution, Min-Ne Min-Max normalization, and gradient computation:
Cekr Σ
|s(i,) - G(S))
(6)
Local Depth Filtering, As shown in Fig 2 the LDF module takes the depth feature D, and event feature E, as inputs to adaptively generate offsets and weights the i-th stage of the decoder (i E (1,2,3}). Following the strategy used in Eqs. 2
this results in the updated depth feature:
Fu (D,; AP; w.).
(7)
In contrast, the modulation scalar Am within W; is learned jointly from the depth and event inputs. Furthermore, to explicitly model regions of dynamic objects, LDF predicts motion mask m, based on E, using sigmoid activation (-) after a single-channel 3 3 convolution Fra (.):
m, = (F (E,)).
(8)
By combining Eqs. and
LDF refines depth with a focus on dynamic regions get:
D D + (1 ())) D;
(9)
Finally, the output D2 from the last LDF module is passed through a 3 x 3 convolutional tail Fr, (.) to generate the dense depth prediction:
D FD.
(10)
Additionally, we introduce motion-aware loss enhance the depth recovery around motion areas:
3
Cmot Σ bb H(D.) - bb, FZ(
(11)
where Z the GT depth, H(--) applies ReLU and a single-channel convolution, and b; is a binary mask with b; 1 if m, exceeds its mean, and 0 otherwise. Fa(-) denotes the downsampling operation.
Discussion. In summary, EMA and LDF differ from previous dynamic convolution methods in two key aspects: (1) Unlike traditional methods that typically rely single-modal and single-path inputs, our approach adopts a molli- and multi-path input design, where key convolutional parameters are derived from different modalities. (2) Our method is data-driven where we use event- based adaptation to address global misalignment and local depth inaccuracies caused by fast motion.
3.3 Loss Function
Given the predicted depth D and GT depth Z with n valid pixels, we adopt commonly used reconstruction loss 36
69
56
55 to formulate the training objective:
1 -ID - 2 [D=-ll.).
Cree =
(12)
By combining the reconstruction loss with the cenettrr--cotttaineed loss Letr in Eq. 6 and motion- aware loss Lrec in Eq. [I] the overall loss function formulated as:
C, = Lree + XLstr + p&mott
(13)
where À and μ are weighting paraeetassmeters that we empirically set to and 0.1, respectively.
5

---

Table I: Basic statistics of the EventDC benchmark.
Dataset
Color Camera
Depth Sensor
Event Camera
Train
Test
Resolution
EventDC-Real
FLIR BFS- U3-31S4C
Ouster OS1-128 LIDAR
DAVIS346
14,845
1,000
320 x 256
EveniDC-SemiSye
Poimtrey Flea2
Velodyne HDL-64E LiDAR
7,094
2,213
1216 256
EventDC-Fulle FullSyn
21,000
500
512 x 256
Sem
FullSyn
Event stream GT depth Figure Visualizations of the proposed EventDC benchmark: WwentDC-Real/Semiiyyll
Color image
Sparse depth
EventDC Benchmark
rely the fusion of color images and sparse depth maps to predict dense depth. However, this approach suffers in highly dynamic environments especially when dealing with fast ego-motion and object motion. This is due to unreliable low-frame-rate RGB images and sparse depth data from motion blur and sampling inconsistencies. Event cameras with the capability to capture high temporal resolution and sensitivity to rapid movements [00 provide an ideal solution to overcome these limitations. By asynchronously recording minute brightness variations, event cameras scenarios where conventional RGB-D sensors fail. In light of these characteristics, we propose an event-based depth completion benchmark that leverages the unique advantages of event data to address the challenges of depth completion in dynamic environments.
Motivation. Traditional depth completion datasets 14
42 65
offer accurate depth information in dynamic
Data Collection. Tab. I provides overview of the sensors used in the datasets with their respective specifications. EventDC -Rea is a real-world dataset in which color images and event frames are captured using the FLIR BFS-U3-31 truth (GT) depth is acquired from a 128-line Ouster LiDAR, and the sparse depth is derived from its 16 sub-lines. EventDC-Senne SemiiSn is semi-synthetic dataset based KITTI 11). The sparse depth and GT depth come from the raw data of KITTI. For the color images, we apply radial motion blur by progressively scaling and transforming the image around its center to simulate motion blur effect with adjustable strength and step count. Additionally, VID2E 12) is used to generate the event data with frames captured within 15 before and after the current timestamp. EvenDDC- FulllSyl is fully synthetic dataset generated using the CARLA simulator (6). The color images are processed similarly with radial motion blur. Finally, facilitate model training, the resolution of all datasets has been cropped to multiples of 32. Fig. 3 presents visual examples from these three datasets.
camera and the DAVIS346 sensor, respectively. The ground
5 Experiment
Metric and Implementation Detail. Following previous depth completion methods [14 46 65| 26). we adopt RMSE (mm). MAE (mm), REL, and threshold accuracy 8 (%) as evaluation metrics. Refer to the appendix for their full definitions. We implement EventDC using the PyTorch framework and conduct training on two NVIDIA RTX 4090 GPUs using the Distributed Data Parallel strategy for efficiency. Optimization is performed with the AdamW optimizer (II) in conjunction with the OneCycle learning rate policy [43]. The training process begins with warm-up stage that linearly increases the learning rate from 0.00002 to 0.001 over the first 10% of iterations. Subsequently, a cosine annealing schedule gradually decays the learning rate to a final value of 0.0002. The batch size is set to per GPU. addition, to further enhance model performance, we employ a set of data augmentation strategies [46 29]. including random horizontal flip, rotation, cropping, and color jitter.

---

Table 2: Quantitative depth completion comparisons on the EventDC- dataset.
Method
RMSE 1 MAE
REL
81.05
01.10
81.15
Venue
CSPN 2
858.5
284.6
0.0386
90.0
94.4
96.0
ECCV 2018
S2D 33
984.1
410.8
0,0565
82.1
90.3
93,4
ICRA 2018
FusionNet
658.1
262.4
0.0384
93.9
96.0
MVA 2019
RigNet 61
685.4
234.6
0.0336
87.5
92.3
95.4
ECCV 2022
DySPN 27
700.1
223.7
0.0285
91.3
95.1
96.6
AAAI 2022
Prompting
38
670.7
205.1
0.0252
92.1
95.7
97.0
CVPR 2024
OGNI-DC 78
709.7
231.1
0.0294
90.9
94.8
96.4
ECCV 2024
SigNet 66
906.4
348.1
0.0345
83.5
90.6
93.2
CVPR 2025
LPNet 55
911.2
389.0
0.0472
83.6
90.4
93.3
arXiv 2025
EventDC (our)
574.0
179.0
0.0242
92,9
96.3
97.5
Improvement
0.0010
0.6
Color image
Event
Prompting
OGNI-DC
EventDC
Figure Depth error comparisons on EventDC-Real. Warmer color indicates higher error.
5.1
Comparisons with State-of-the-arts
In this section, we compare our EventDC with well-known methods: CSPN 22 S2D 33
FusionNet [49. RigNet [61], DySPN 27), Prompting (38), OGNI-DC (88,, SigNet [66, and LPNet (55) For
fair comparison, we retrain all methods from scratch on the proposed benchmark. Note that BPNet
[47] TPVD [65], and DMD*C [26] are excluded from the comparison. This is because they require additional camera parameters during training which are not available in settings.
EventDC-Real. We first evaluate the proposed EventDC on EventDC-Real, a real-world dataset collected using various devices such as handheld sensors and robotic platforms. The numerical results are summarized Tab 2 Our EventDC achieves the overall lowest errors while maintaining the highest accuracy across the board. For example, it outperforms the second-ber best method by 84.1 mm RMSE, 26.1 mm in MAE, 0.001 in REL, and points ò1.05- Compared to post-refinement methods such as CSPN (2 and DySPN 27 our EventDC without any post-processing consistently achieves better performance. Even when compared to the large-scale depth foundation model Prompting (38) approach achieves superior results with significantly fewer model parameters. Fig. presents the comparisons depth error. It clearly shows that our EventDC produces more accurate depth results especially around moving objects.
EventDC-SemiSyn. To further validate the effectiveness of EventDC, we evaluate it on EventDC- SemiSyn, semi-synthetic dataset comprising synthetically generated event frames and color images rendered under highly dynamic conditions. As reported Tab. B our EventDC continues to deliver outstanding results across all evaluation metrics. On average, it outperforms recent methods: Prompting (88] OGNI-DC (78), and LPNet (55) by 18.9%, 30.8% and 27.7% in RMSE, MAE, and REL,, respectively. and by 3.9, 1.6, and 0.9 percentage points in ò1.0s. 81.10- and 81.15, respectively. As illustrated in Fig. 5 our EventDC effectively reconstructs accurate depth details and structural consistency even under highly dynamic scenes.
EventDC-FullSynn Apart from the real and semi-synthetic settings, we also validate EventDC on the fully synthetic dataset, WhentDC-FullSyya to further assess its generalization capability under diverse scenarios. As shown in Tab our EventDC consistently outperforms all competing approaches by large margins. For example, it surpasses the second- best approach by 53.5 mm in RMSE and 19.4 mm in MAE. addition, achieves 13.0% improvement in REL compared to the foundation model-based Prompting [38] These results demonstrate the robustness of our EventDC in reducing both absolute and relative errors. Fig. ( shows that our EventDC yields more refined details and sharper object boundaries than others, which highlight its effectiveness in fully synthetic scenarios.
7

---

Table 3: Quantitative comparisons on the EventDC-SemiSyn dataset.
Color Imagge
Method
RMSEL
MAEL
REL
01,0
61.00
11.1
CSPN
989.8
262.8
0.0189
94.6
97.2
98.1
Fooert Promping OON--CC FvebC
S2D 33
1097.3
366.4
0.0237
91.0
96.4
97.9
FusionNet
49
877.6
333.1
0.0258
92.6
98.2
98.8
RigNet 6
858.2
216.4
0.0156
95.1
97.8
98.1
DySPN 27
897.7
207.5
0.0149
95.9
97.8
98.6
Prompting
18
873.9
291.1
0.0198
92.6
97.1
98.4
OGNI-DC
78
832.0
210.5
0.0143
95.7
98.0
98.7
SigNet 66
1065.4
321.3
0.0226
91.1
97.0
98.1
I.PNet 55
1283.4
416.3
0.0242
90.0
95.3
97.2
EventDC (ours)
778.8
196.2
0.0134
96.7
98.4
99.0
Improvement f
53.2
11.3
0.0009
0.8
0.2
0.2
Figure 5: Visual results.
Table 4: Quantitative comparisons on the EventDC-FullSyy dataset.
Cylori malg
Method
RMSEJ
MAE]
REL
61.05
61.10"
61.15
CSPN 2
864.9
399.5
0.1193
62.7
80.9
87.6
Event
S2D 33
899.0
376.2
0.1243
69.8
83.8
89.1
FusionNet 49
670.6
230.9
0.0931
77.3
86.6
90.4
Promping
RigNet
723.4
166.3
0.0578
81.1
91.6
92.8
DySPN 2
679.8
165.6
0.0646
87.2
92.6
94.6
Prompting
38
709.7
180.9
0.0538
90.7
93.8
95.3
OGNLD2 EventCC
OGNI-DC
78
673.7
162.5
0.0578
87.8
93.0
95.1
SigNet 66
904.5
349.2
0.0902
76.3
84.1
90.3
I.NNet 55
920.2
357.3
0.0943
75.1
85.9
90.3
EventDC (ours)
620.2
143.1
0.0468
92.1
95.5
96.8
Improvement t
53.5
19.4
0.0070
1.4
1.7
1.5
Figure 6: Visual results.
Complexity Analysis. Tab. presents the com- plexity comparisons between our EventDC and other competing methods in terms of model param- eters (Param.), memory consumption (Memo.), and inference time. Our EventDC not only achieves outstanding performance, but also maintains com- petitive efficiency. In particular, compared to the second-best method Prompting (38), our EventDC achieves a significantly lower RMSE by 96.7 mm with only about one-eighth the number of param- eters and one-third the memory.
Table 5: Complexity on EventDC-Real.
Param.
Memo.
Time
RMSE
Method
(M)
(GB)
(ms)
(mm)
DySPN 27
26.3
0.9
9.8
700.1
RigNet 66
65.2
2.3
26.5
685.4
Prompting 38
326.9
4.1
670.7
OGNI-DC 78
84.4
3.7
314.1
709.7
LPNet [5
29.6
1.1
18.4
911.2
EventDC (our)
43.2
1.5
41.5
574.0
5.2 Ablation Studies
Tab. summarizes the ablation results EventDC-Real, EventDC-i serves as UNet-style baseline that takes only sparse depth as input and employs additive skip connections.
(1) EventDC-ii further develops this approach by utilizing RGB images and integrating RGB-D features through additive fusion. Although depth input sparse, RGB offers rich structural and semantic details. This leads to significant decrease in error and substantial gains in accuracy. For example, the RMSE reduced by 41.3 mm and the MAE by 32.1 mm. EventDC-iii enhances support for event streams, which are advantageous because of their fine temporal detail and motion sensitivity. Consequently, this makes them very effective in dynamic environments where they supplement depth data. EventDC-iv combines all three modalities to give consistent improvements across all evaluation metrics. Specifically, surpasses
baseline by 12.2%, 20.7%, and 8.5% RMSE, MAE, and REL, respectively. It concurrently improves 61.05. 81.10. and 81.15 by 1.0, 0.3, and 0.2 percentage points. These results underscore the effectiveness of multi-modal fusion, where the integration of complementary modalities enables more accurate and complete depth reconstruction.

---

Table Ablations on EventDC-Real. DConv/Ene/Decc: dynamic convolution/eeccoderreeeod..
Modality RGB
DConv
EMA Enc
LDF Dec
EventDC
RMSE
MAE
REL
81.05
61.30
81.1
Depth
Event
Enc
Dec
727.2
283.2
0.0341
89.3
94.4
96.2
ii
685.9
251.1
0.0328
89.8
94.5
96.2
696.0
244.3
0.0314
90.0
94.5
96.3
iv
638.3
224.7
0.0312
90.3
94.7
96.4
v
628.8
219.5
0.0292
91.0
95.0
96.7
602.8
196.1
0.0276
91.7
95.6
97.1
vii
630.6
219.8
0.0295
91.0
94.8
96.5
viii
605..4
198.3
0.0279
91.5
95.6
97.1
ix
574.0
179.0
0.0242
92.9
96.3
97.5
Depth feaure
LDF
LDF
Probability density
40
30
Color imaae
Event
Error map
Emrr map 2
10
-02
-0.1
0.1
Pixel -0.2
-0.1
0.0
0.1
0.2
(b) Results around moving objects Figure 7: Statistical and visual comparative analyses of the proposed EMA and LDF modules.
Hislogram comparison
(2) EventDC-v to EventDC-ix conduct ablation studies to examine the impact of dynamic convolution (DConv), EMA, and LDF in the encoder (Enc) and decoder (Dec) stages. Specifically, the introduction of DConv in EventDC-v brings notable benefits. Furthermore, EventDC-vi with EMA further reduces RMSE by 26 mm. These results validate the efficacy of our event-based adaptive alignment strategy. Fig. a) compares the distributions of RGB-D features with and without EMA. EMA works as intended in promoting better alignment between the two modalities with more consistent feature representations. Similarly, EventDC-viii with LDF outperforms EventDC-vil with DConv by 25.2 mm. This demonstrates its superior ability recover ane-ggained local depth which is further evident in Fig. (b). Finally, EventDC-ix which integrates both EMA and LDF modules achieves the best overall performance. It reduces RMSE by 16.0% (from 683.3 mm) and MAE by 20.3% (from 224.7 mm). In summary, each component contributes positively to the overall performance gains.
6 Conclusion
We propose EventDC in this work. Our EventDC the first depth completion framework that tackles the challenges of dynamic scenes by harnessing the unique strengths of event data. To mitigate the adverse effects of fast ego-motion and object motion, our EventDC incorporates two event-driven modules: wvent-modulated alignment and local depth filtering. These modules, supported by two dedicated loss constraints, address global misalignment and local depth inaccuracies, respectively. To further support research in this area, construct the first benchmark for event-based depth completion comprising one real-world and two synthetic datasets. Extensive experiments demonstrate the effectiveness of our EventDC and its superior performance in challenging dynamic environments.
Limitation and Broader Impact. Despite achieving promising results in dynamic scenes, our EventDC relies on high-quality event data and precise sensor alignment that may not be easily attainable in all real-world settings. The EMA and LDF modules introduce additional computational costs, potentially limiting deployment diversity of our real-world dataset are limited, and future work is needed evaluate generalization across more diverse environments and motion patterns. Despite these limitations, our EventDC offers a step forward robust depth perception under motion blur and rapid dynamics with potential applications in autonomous driving, robotics, AR/VR, etc. By introducing a dedicated benchmark, we aim to promote research in event-based depth completion. As with all perceptual systems, responsible deployment requires attention to reliability, faimeess, and safety in complex real-world conditions.
eecource-constraingg
devices. Moreover, the scale and

---

Acknowledgments
This research/project supported by the National Research Foundation, Singapore, under its NRF- Investigatorship Programme (Award ID. NRF-NRF109-0008) and the Tier grant T1-251RES2305 from the Singapore Ministry of Education.
References
[1] Yun Chen, Bin Yang, Ming Liang, and Raquel Urtasun. Learning joint 2d-3d representations for depth completion. In ICCV, pages 10023-10032, 2019.
[2] Xinjing Cheng, Peng Wang, and Ruigang Yang. Learning depth with convolutional spatial propagation network. In ECCV, pages 103-119, 2018.
[3] Xinjing Cheng, Peng Wang, and Ruigang Yang. Learning depth with convolutional spatial propagation network. IEEE Transactions Pattern Analysis and Machine Intelligence, 42(10): 2361-2379, 2019.
[4] Xinjing Cheng, Peng Wang, Chenye Guan, and Ruigang Yang. Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion. In AAAI, pages 10615-10622, 2020.
[5] Jifeng Dai, Haozhi Qi. Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In ICCV, pages 764-773, 2017.
[6] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. Carla: An open urban driving simulator. In CoRL, pages 1-16. PMLR, 2017.
[7] Abdelrahman Eldesokey, Michael Felsberg, and Fahad Shahbaz Khan. Confidence propagation through cnns for guided sparse depth regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(10):2423 -2236, 2020.
[8] Guillermo Gallego, Jon EA Lund, Elias Mueggler, Henri Rebecq, Tobi Delbruck, and Da- vide Scaramuzza. Event-based, 6-dof camera tracking from photometric depth maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10):2402-241,, 2017.
[9] Guillermo Gallego, Henri Rebecq, and Davide Scaramuzza. A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation. In CVPR, pages 3867-3876, 2018.
[10) Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, Jörg Conradt, Kostas Daniilidis, et al. Event- based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (1):154-180, 2020.
[11] Daniel Gehrig and Davide Scaramuzza. Low-latency automotive vision with event cameras. Nature, 429(8014): 1034-10044444 2024.
[12] Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrió, and Davide Scaramuzza. Video to events: Recycling video datasets for event cameras. In CVPR, pages 3586-3595, 2020.
[13] Daniel Gehrig, Michelle Rüegg, Mathias Gehrig, Javier Hidalgo-Carrió, and Davide Scaramuzza. Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction. IEEE Robotics and Automation Letters, 6(2):2822-282)) 2021.
[14] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, pages 3354-3361, 2012.
[15] Suman Ghosh and Guillermo Gallego. Event-based stereo depth estimation from ego-motion using ray density fusion. arXiv preprint arXiv:2210.08977 2022.
[16] Jakub Gregorek and Lazaros Nalpantidis. Steeredmarigold: Steering diffusion towards depth completion of largely incomplete depth maps. arXiv preprint MtXv::2409.10000 2024.
10

---

[17] Javier Hidalgo-Cami Carrió. Daniel Gehrig, and Davide Scaramuzza. Learning monocular dense depth from events. In 3DV, pages 534-542. IEEE, 2020.
[18] Diego de Oliveira Hitzges, Suman Ghosh, and Guillermo Gallego. Derd-net: Learning depth from event-based ray densities. arXiv preprint arXiv:2504.1588 2025.
[19] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-eecitation networks. In CVPR, pages 7132-7141, 2018.
[20] Mu Hu, Shuling Wang. Bin Li, Shiyu Ning, Li Fan, and Xiaojin Gong. Penet: Towards precise and efficient image guided depth completion. In ICRA, 2021.
[21] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. In Neur/PS, 2015.
[22] Yunho Jeon and Junmo Kim. Active convolution: Learning the shape of convolution for image classification. In CVPR, pages 4201-4209, 2017.
[23] Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Lue Gool. Dynamic filter networks. In NeurlPS, 2016.
[24] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint
2016.
[25] Jason Ku, Ali Harakeh, and Steven Waslander. defense of classical image processing: Fast depth completion on the cpu. In CRV, pages 16-22. IEEE, 2018.
[26] Yingping Liang, Yutao Hu, Wenqi Shao, and Ying Fu. Distilling monocular foundation model for fine-grained depth completion. arXiv preprint arXiv:2503. /6970, 2025.
[27] Yuankai Lin, Tao Cheng, Qi Zhong, Wending Zhou, and Hua Yang. Dynamic spatial propagation network for depth completion. In AAAI, pages 1638-1646, 2022.
[28] Yuankai Lin, Hua Yang, Tao Cheng, Wending Zhou, and Zhouping Yin. Dyspn: Learning dynamic affinity for image-guided depth completion. IEEE Transactions on Circuits and Systems for Video Technology, pages 1-1, 2023.
[29] Lina Liu, Xibin Song, Xiaoyang Lyu, Junwei Diao, Mengmeng Wang, Yong Liu, and Liangjun Zhang. Fefr-net: Feature fusion based coarse-to-fine residual learning for depth completion. In AAAI, pages 2136-2144, 2021.
[30] Xu Liu, Jianing Li, Jinqiao Shi, Xiaopeng Fan, Yonghong Tian, and Debin Zhao Exent-based monocular depth estimation with recurrent transformers. IEEE Transactions on Circuits and Systems for Video Technology, 2024.
[31] Iya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint 17110: 2017.
[32] Kaiyue Lu, Nick Barnes, Saeed Anwar, and Liang Zheng. From depth what can you see? depth completion via auxiliary image reconstruction. In CVPR, pages 11306-11315, 2020.
[33] Fangchang Ma and Sertac Karaman. Sparse-to-dense: Depth prediction from sparse depth samples and a single image. In ICRA, pages 4796-4803. IEEE, 2018.
[34] Manasi Muglikar, Diederik Paul Moeys, and Davide Scaramuzza. Event guided depth sensing. In 3DV, pages 385-393. IEEE, 2021.
[35] Tianbo Pan, Zidong Cao, and Lin Wang. Srfnet: Monocular depth estimation with fine-grained structure via spatial reliability-oriente fusion of frames and events. ICRA, pages 10695- 10702. IEEE, 2024.
[36] Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon. Non-local spatial propagation network for depth completion. In ECCV, 2020.

---

[37] Jin-Hwi Park and Hae-Gon Jeon. A simple yet universal framework for depth completion. In NeurIPS, 2024.
[38] Jin-Hwi Park, Chanhwi Jeong, Junoh Lee, and Hae-Gon Jeon. Depth prompting for sensor- 2024.
agnostic depth estimation. In CVPR, pages
[39] Henri Rebeeq, Guillermo Gallego, Elias Mueggler, and Davide Scaramuzza. Emvs: Event-based multi-view stereo-30 3d reconstruction with an event camera in real-time. International Journal 2018.
of Computer Vision, (166(1)):1394111
[40] Shuwei Shao, Zhongeai Pei, Weihai Chen, Xingming Wu, and Zhengguo Li. Nddepth: Normal- distance assisted monocular depth estimation. In ICCV, pages 7931-7940, 2023.
[41] Shintaro Shiba, Yannick Klose, Yoshimitsu Aoki, and Guillermo Gallego. Secrets of event-based optical flow, depth and ego-motion estimation by contrast maximization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[42] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rghd images. In ECCV, pages 746-760. Springer, 2012.
[43] Leslie N Smith and Nicholay Topin. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial Intelligence and Machine Learning for -ulii-domain Operations Applications, pages 369-386. SPIE, 2019.
[44] Shuran Song, Samuel Lichtenberg, and Jianxiong Xiao. Sun rgb-d: A rgb-d scene understand- ing benchmark suite. In CVPR, pages 567-576, 2015.
[45] Xibin Song, Yuchao Dai, Dingfu Zhou, Liu Liu, Wei Li. Hongdong Li, and Ruigang Yang. Channel attention based iterative residual learning for depth map super-resolution. CVPR, pages 5631-5640, 2020.
[46] Jie Tang, Fei-Peng Tian, Wei Feng, Jian Li, and Ping Tan. Learning guided convolutional network for depth completion. IEEE Transactions on Image Processing, 30:1 1111116-11 16-1119, 2020.
[47] Jie Tang, Fei-Peng Tian, Boshi An, Jian Li, and Ping Tan. Bilateral propagation network for depth completion. In CVPR, pages
2024.
[48] Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, and Andreas Geiger. Sparsity invariant cnns. In 3DV, pages 11-20, 2017.
[49] Wouter Van Gansbeke, Davy Neven, Bert De Brabandere, and Luc Van Gool. Sparse and noisy lidar completion with rgb guidance and uncertainty. In MVA, pages 1-6, 2019.
[50] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv: 1710. 10903, 2017.
[51] Massimiliano Viola, Kevin Qu, Nando Metzger, Bingxin Ke, Alexander Becker, Konrad Schindler, and Anton Obukhov. Marigold-dc: Zero-shot monocular depth completion with guided diffusion. arXiv preprint arXiv:2412.13338 2024.
[52] Jiyuan Wang, Chunyu Lin, Lang Nie, Shujun Huang, Yao Zhao, Xing Pan, and Rui Ai. Weath- erdepth: Curriculum contrastive learning for self- supervisdd depth estimation under adverse weather conditions. In ICRA, page 4976-49779 IEEE, 2024.
[53] Jiyuan Wang, Chunyu Lin, Cheng Guan, Lang Nie. Jing He, Haodong Li, Kang Liao, and Yao Zhao. Jasmine: Harnessing diffusion prior for self-supervised depth estimation. arXiv preprint arX1v:2503.15905, 2025.
[54] Ji Wang, Chunyu Lin, Lei Sun, Rongying Liu, Lang Nic, Mingxing Li, Kang Liao, Xiangxiang Chu, and Yao Zhao. From editor to dense geometry estimator. arXiv preprint arXiv:2509.044 04338, 2025.
[55] Kun Wang. Zhiqiang Yan, Junkai Fan, Jun Li, and Jian Yang. Learning inverse laplacian pyramid for progressive depth completion. arXiv preprint arXtv:2500.07289, 2025.
12

---

[56] Yufei Wang, Bo Li, Ge Zhang, Qi Liu, Tao Gao, and Yuchao Dai. LITu: Long-short range recurrent updating networks for depth completion. In ICCV, pages 9422-94333 2023.
[57] Yufei Wang, Ge Zhang, Shaoqian Wang, Bo Li, Qi Liu, Le Hui, and Yuchao Dai. Improving depth completion via depth feature upsampling. In CVPR, pages 21 104-21113, 2024.
[58] Hexiang Wei, Jianhao Jiao, Xiangcheng Hu, Jingwen Yu, Xupeng Xie, Jin Wu, Yilong Zhu, Yuxuan Liu, Lujia Wang, and Ming Liu. Fusionportablev2: A unified multi- sensor dataset for generalized slam across diverse platforms and scalable environments. The International Journal of Robotics Research, page 02783649241 1003525, 2024.
[59] Zheyuan Xu, Hongche Yin, and Jian Yao. Deformable spatial propagation networks for depth completion. In ICIP, pages 913-917. IEEE, 2020.
[60] Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Guangyu Li, Jun Li, and Jian Yang. Learning complementary correlations for depth super-resolution with incomplete data in real world. IEEE Transactions on Neural Networks and Learning Systems, 2022.
[61] Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang. Rignet: Repetitive image guided network for depth completion. In ECCV, pages 214-230, 2022.
[62] Zhiqiang Yan, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang. Rignet++: Efficient repetitive image guided network for depth completion. arXiv preprint arXiv:2309.00655. 2023.
[63] Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang. Desnet: Decomposed scale-consistent network for unsupervised depth completion. In AAAI, pages 3109-3111 2023.
[64] Zhiqiang Yan, Yupeng Zheng, Kun Wang, Xiang Li, Zhenyu Zhang, Shuo Chen, Jun Li, and Jian Yang. Learnable differencing center for nighttime depth perception. arXiv preprint arXiv:2306. 14538, 2023.
[65] Zhiqiang Yan, Yuankai Lin, Kun Wang, Yupeng Zheng, Yufei Wang, Zhenyu Zhang, Jun Li, and Jian Yang. Tri-perspective view decomposition for geometry-aware depth completion. In CVPR, pages 4874-4884, 2024.
[66] Zhiqiang Yan, Zhengxue Wang, Kun Wang, Jun Li, and Jian Yang. Completion as enhancement: degradation- aware selective image guided network for depth completion. arXiv preprint 2rXIv:2412.19225. 2024.
[67] Zhu Yu, Zehua Sheng, Zili Zhou, Lun Luo, Yuan Cao, Hong Gu, Huaqi Zhang, and Hui-Liang Shen. Aggregating feature point cloud for depth completion. In ICCV, pages 8732-8743, 2023,
[68] Xin Zhang, Liangxiu Han, Tam Sobeih, Lianghao Han, and Darren Dancey. A novel spike transformer network for depth estimation from event cameras via cross-modality knowledge distillation. arXiv preprint arXi::240..17335 2024.
[69] Youmin Zhang, Xianda Guo, Mattco Poggi, Zheng Zhu, Guan Huang, and Stefano Mattoccia. Completionformer: Depth completion with convolutions and vision transformers. In CVPR, pages 18527-18536. 2023.
[70] Shanshan Zhao, Mingming Gong, Huan Fu, and Dacheng Tao. Adaptive context-aware multi- modal network for depth completion. IEEE Transactions on Image Processing, 2021.
[71] Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Zudi Lin, and Hanspeter Pfister. Discrete cosine transform network for guided depth map super-resolution. In CVPR, pages 5697-5707, 2022.
[72] Zixiang Zhao, Jiangshe Zhang, Xiang Gu, Chengli Tan, Shuang Xu, Yulun Zhang, Radu Timofte, and Luc Van Gool. Spherical space feature decomposition for guided depth map super-resolution. In ICCV, pages 12547-12558, 2023.
[73] Wending Zhou, Xu Yan, Yinghong Liao, Yuankai Lin, Jin Huang, Gangming Zhao, Shuguang Cui, and Zhen Li. Bev de: Bird's-eye view assisted training for depth completion. In CVPR, pages 2023.
13

---

[74] Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, and Kostas Daniilidis. Unsupervised event-based learning of optical flow, depth, and egomotion. In CVPR, pages 989-997, 2019.
[75] Junyu Zhu, Lina Liu, Bofeng Jiang, Feng Wen, Hongbo Zhang, Wanlong Li, and Yong Liu. Self-super event-based monocular depth estimation using cross-modal consistency. In IROS, pages 7704-7710. IEEE, 2023.
[76] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In CVPR, pages 9308 031699936,1111 2019.
[77] Alex Zihao Zhu, Nikolay Atanasov, and Kostas Daniilidis. Event-based visual inertial odometry. In CVPR, pages 5391-5399, 2017.
[78] Yiming Zuo and Jia Deng. Ogni-de: Robust depth completion with optimization neural iterations. In ECCV, pages 78-95. Springer, 2024.
14

---

A Metriis.
We adopt the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Relative Error (REL), and threshold accuracy Pg as evaluation metrics, where is set to 1.05, 1.10, and 1.15, The definitions of these metrics shown in Tab.
Table 7: Definition of evaluation metrics.
Given the predicted depth D and GT depth Z with n valid pixels:
RMSE:
Σ (D - 2
MAE:
+ED: Z
REL:
1 Σ D /
6g:
n 9: max
Z
D Z
<
0
EventDC-Real
EventDC-SemlSyn
EventDC-FullSyn
Color image
Sparse depth
Event stream
depth
Figure 8: More visual examples of the proposed event event-based depth completion benchmark.
B More Visualizations
Fig. presents some RGB-D-Event examples from our wrent-based depth completion benchmark, showeasing its high quality and strong cross-modal consistency, as well as the close correlation among the RGB, depth, and event modalities. Figs. 9 10 RventDC-Real, FventDC-SemiSyn, and FventDC-FullSyy datasets. These results further validate that our approach effectively improves depth predictions through the event-driven module designs.
and 1T show visual comparisons on the
15

---

Color image
Event
Prompting
OGNI-DC
EventDC
Figure 9: More depth error comparisons on EventDC-Real, Warmer colors indicate higher errors.
Prompting
OGNI-DO
EventDC
Figure 10: More depth visualization comparisons the proposed ExentDDCSSmiSSnn dataset.
Prompting
OGNI-DC
EventDC
Figure 11: More depth visualization comparisons on the proposed EventDC-FullSyn dataset.
16

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The main claims made in the abstract and introduction have accurately reflected the paper's 's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: Please refer the section of "Limitation and Broader Impact'.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
17

---

Justification:
This paper does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
We have described the steps taken make our results reproducible verifiable.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reprodueibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
18

---

Answer:
[No]
Justification:
The data and source codes will publicly available upon acceptance.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips... cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/ cpippi.ccppulli//uuuii wwwpc//ggue////eee CoodeSubeiiieeee ssi wsionnolli onnolicy)))) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Please refer to the experiment section.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: Please refer to the experiment section.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, at least for the experiments that support main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
19

---

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Please refer the experiment section.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g.. preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: :ttta::://neurips.t http://s......b.....hhhccccoo..d
Answer:
[Yes]
Justification:
We have made sure preserve anonymity.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there is a special consid- eration due to laws or regulations their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: Please refer the section of "Limitation and Broader Impact'.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to
20

---

generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
there are negative socictal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: This paper poses such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: CC-BY 4.0.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, wwppppswpptseeee whworee.wtcche....c.. com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
this information is not available online, the authors are encouraged to reach out to the asset's is creators.
13. New assets
Question: Are new assets introduced in the paper well documented and the documentation provided alongside the assets?
21

---

Answer: [Yes]
Justification: We have communicated the details of the dataset/code/moodl as part of our submissions via structured templates.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification: This paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According the NeurIPS Code of Bthies, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for rescarch with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: This paper does involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMSs if it is an important, original, or non-standard component of the methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality the research, declaration is required.
Answer: [NA]
22

---

The core method development in this research does not involve LLMs as any important, original, or non-standard components.
Justification:
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non- standadd components.
Please refer to our LLM policy hattps://neurips... 220p://ee////oofeeeeee/2/22///// for what should or should not be described.
23


Reviews:
[{"review_id": "OX2BQ2jzyG", "reviewer": "Reviewer_aJhM", "summary": "This paper presents a novel event-driven dynamic scene depth completion pipeline. The core research question is how to effectively predict dense depth from event camera-based sparse measurements in highly dynamic environments. This task is particularly difficult because object movement can create severe artifacts on RGB cameras and LiDAR measurements. To address these issues, this work introduces EventDC, which leverages the high temporal resolution and motion sensitivity of event cameras.  Its key design incorporates two main components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). Crucially, the paper also establishes the first event-based depth completion benchmark, comprising real-world, semi-synthetic, and fully synthetic datasets. The evaluation shows that EventDC achieves competitive efficiency and accuracy.", "questions": "- Is it possible to adjust the network architecture to trade-off between accuracy and latency to fit different deployment environments?", "limitations": "The paper acknowledges that while EventDC achieves promising results, its Event-Modulated Alignment and Local Depth Filtering modules introduce additional computational costs. This could potentially limit deployment on resource-constrained devices. While EventDC maintains competitive efficiency, its inference time is 41.5 ms. This computational latency directly determines the maximum frequency at which the system can generate depth inferences in a real-time setting. However, the paper does not explicitly discuss the implications of network latency on the overall system's real-time performance, nor does it detail how such latency might affect the amount of event information available for processing during different inference cycles.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The proposed pipeline addresses depth completion in the dynamic environment. This is very important for real-world applications such as autonomous vehicles and augmented reality.\n- The source code and datasets are promised to be released.\n\nWeaknesses:\n- My main concern is on the latency of the pipeline. The proposed work targets a highly dynamic environment, but the proposed pipeline takes >40ms to run based on Table 5.", "was_revised": true, "final_justification": "The author has sufficiently addressed my primary concerns around the pipeline latency. The reported latency of latency of 41.5 ms seems reasonable for the targeted application. The author has also recognized several key limitations of the current pipeline. My final rating score remain unchanged.", "rebuttal": "**1. Weakness (pipeline latency) and Question (trade-off design):**\n\nWe fully agree that the task of predicting dense depth from event camera-based sparse measurements in highly dynamic environments is *particularly difficult*. Our paper proposes a framework to solve this very challenging task for the first time. We hope our work serves as a foundation for future research and encourages continued advancements across multiple aspects.\n\nCurrently, our method primarily focuses on addressing **RGB-D misalignment** and improving **depth accuracy around moving objects**. However, we acknowledge that the present latency (41.5 ms) remains insufficient for the demands of highly dynamic scenarios. Following the suggestion of the reviewer, we explored several trade-off designs by reducing the channels of EventDC to half or one-quarter at each encoder stage, and by replacing the encoder with the lightweight EfficientNet-B0. The results in Table R1 show significantly reduced latency at the trade-off of some increase in error. Nonetheless, the performance remains superior to the suboptimal Prompting method. \n\nWe thank the reviewer for this valuable suggestion, and we will incorporate these additional results in our final paper.\n\n*Table R1: Complexity comparisons on EventDC-Real.*\n| Method          | Param. (M) | Time (ms) | RSME (mm) |\n|---|:---:|:---:|:---:|\n| DySPN            | 26.3 | 9.8     | 700.1 |\n|Prompting        |326.9 | 39.5  | 670.7 |\n| ***Raw EventDC***   | 43.2  | 41.5  | **574.0** |\n| *½ channel*        | 22.8  | 25.4  | 613.3  |\n| *¼ channel*        | **11.7**  | 11.2  | 631.6  |\n| *EfficientNet-B0* |12.8   | **8.3**   |  617.5 |\n\n**2. Limitation:**\n\nYes, the current latency of 41.5 ms (\\~20.1 FPS) on EventDC-Real does not meet the real-time threshold. Since the color camera in EventDC-Real operates at 30 FPS, our model currently lags in a temporal gap of around 10 frames or approximately 300 ms of unprocessed event information. Moreover, a frame rate closer to 60 FPS (\\~16.6 ms latency) is often desirable for more responsive performance in high-dynamic environments. We will include Table R1 in the final paper to improve clarity. In future work, we plan to explore more efficient architectural designs. We will also investigate optimization strategies to reduce latency and move closer to real-time performance.\n\n**3. Paper Formatting Concerns:**\n\nWe thank the reviewer for checking our paper meticulously. We will correct the typos (Table 1 *FLIR BFS-U3-31S4C*, Line 236 *Prompting*), proofread and spellcheck the paper in the final version.\n\n*Finally, please let us know if you have any further questions or suggestions that could help us improve the paper!*"}, {"review_id": "D3Ez5jPrzA", "reviewer": "Reviewer_1o7Y", "summary": "This paper proposes an EventDC model that leverages RGB, LiDAR, and event images to generate dense depth maps. Two key components—event-modulated alignment and local depth filtering—are designed based on deformable convolution. Meanwhile, three datasets are constructed, including a real-world dataset, a semi-synthetic dataset, and a fully synthetic dataset. Experiments on these datasets demonstrate the effectiveness of the proposed method.", "questions": "1. In Figure 2, should the Event stream E be positioned in the middle while the sparse depth S is at the bottom?\n\n2. It is suggested to unify the notation of the offset in equations (1), (3a), and (3b).\n\n3. Why are the weight designs in the deformable convolution different between the encoder and decoder? What would happen if they used the same design?\n\n4. It is expected that EventDC improves depth estimation around moving objects, but in Figure 4, it appears that improvements are more pronounced in the distant background, which might be less dynamic. Could you explain why? It would be helpful to see more results on dynamic objects.\n\n5. How does the model perform without the event stream but with the designed deformable convolutions?", "limitations": "The rationale behind different deformable convolution designs in encoder and decoder is unclear. \n\nThe results mainly show improvements on static backgrounds rather than dynamic objects, which contradicts the main goal. \n\nThere is no ablation study to isolate the contribution of the event stream.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. This work is likely the first to utilize event images specifically for depth completion with dynamic environments.\n2. The proposed method is both reasonable and effective.\n3. The paper is well-written and easy to follow.\n4. Additionally, relevant datasets are provided.\n\nWeaknesses:\n1. Figure 2’s layout is confusing, affecting clarity of the network structure.\n2. Offset notations in equations (1), (3a), and (3b) are inconsistent.\n3. Lack of explanation for different deformable convolution weight designs in encoder and decoder.\n4. Improvements seem more on static backgrounds than on dynamic objects, which contradicts the main goal.\n5. Missing evaluation of model performance without the event stream input.", "was_revised": true, "final_justification": "Thanks to the author for the reply. Most of my concerns have been resolved. I would like to raise the score.", "rebuttal": "**To Weakness1 and Question1:**\n\nThe rationale behind the current layout is as follows:\n\n*1. Depth branch (S) as the main stream:* In depth completion tasks, the sparse depth input is typically treated as the primary modality, with RGB (I) and Event (E) streams serving as auxiliary sources. Therefore, we place the S branch at the center of the diagram to emphasize its dominant role in predicting the dense depth map.\n\n*2. Symmetry with the decoder:* The depth features extracted from the S branch are progressively refined through the decoding path. To clearly illustrate this symmetric encoder-decoder structure, we align the S branch horizontally with the decoder layers.\n\n*3. Cross-modal guidance via EMA modules:* The EMA modules fuse features from the I and E branches to enhance the S branch at multiple stages. This hierarchical guidance flows into the next layer of the S branch. Maintaining the current top-down layout (I at top, S in center, E at bottom) allows us to clearly visualize this progressive refinement process.\n\nAccording the reviewer's suggestion, we do explore alternative layouts, including swapping the positions of the S and E branches. However, we find that these configurations introduce more visual clutter, particularly due to increased crossing of connection lines, which makes the figure harder to follow. After comparison, we determine that the current configuration offers better visual balance and a more intuitive interpretation of the network architecture.\n\nNevertheless, we agree that Figure 2 can benefit from improved clarity and thank the reviewer for this suggestion. We will revise the figure with a cleaner layout and clearer labeling to enhance readability and ensure that the design intentions are more easily understood.\n\n**To Weakness2 and Question2:**\n\nWe will adopt Δ𝑝 as the unified notation for the offset throughout the paper. This would help avoid confusion and improve the overall readability of the mathematical formulations.\n\n**To Weakness3, Question3, and Limitation1:**\n\nAs shown in Equation (1), the weight term of the deformable convolution in DCNv2 is represented as 𝑤·Δ𝑚, where 𝑤 denotes the learnable convolution kernel and Δ𝑚 is the modulation scalar that adjusts the importance of each sampled position in the offset domain. The *only difference* in the deformable convolution weights between the encoder and decoder lies in the treatment of Δ𝑚: \n\nIn the encoder, Δ𝑚 is set as an identity matrix and does not participate in learning; \n\nIn the decoder, Δ𝑚 is adaptively learned based on event information. \n\nThe reasons for such design are as follows:\n\nAs illustrated in Fig. 2, the EMA modules in the encoder primarily adjust the pixel distributions of RGB and depth features individually using event information. They further align the RGB-D features under the constraint of Equation (6), which helps mitigate misalignment caused by fast ego-motion. In other words, EMA focuses on learning the offset. Furthermore, 𝑤 is automatically learned by the network and Δ𝑚 is fixed as a non-learnable identity matrix.\n\nThe LDF of the decoder pays more attention to depth recovery around dynamic object regions after EMA-based alignment. As a result, it not only learns the offsets near moving objects but also aims to learn the contribution of each position through Δ𝑚. This helps regulate the weighted summation process and promotes more accurate depth estimation around dynamic areas.\n\nAt the code level, making Δ𝑚 learnable in the encoder is straightforward to implement. As shown in Table R2, two sets of comparison experiments are conducted based on the reviewer's suggestion.  The results indicate that a learnable Δ𝑚 in the encoder has little impact on performance. We thank the reviewer for this valuable suggestion and will clarify this point in the final paper.\n\n*Table R2: RMSE Results on EventDC-Real.*\n|Δ𝑚|1st| 2nd|3rd|average|\n|---|:---:|:---:|:---:|:---:|\n| non-learnable | 574.0 | 576.3 | 575.8 | 575.4 |\n| learnable        | 574.6 | 573.5 | 577.2 | 575.1 |\n\n\n**To Weakness4, Question4, and Limitation2:**\n\n*1. Focusing on the EventDC-Real dataset (i.e., Fig. 4):*\n\nThe event stream reveals that dynamic regions include not only the truck but also background elements such as shaking trees. Depth unreliability often occurs both within these dynamic areas and at their boundaries with the background. As shown in Fig. 4, our proposed method gives better results in event-indicated areas and their transitions to the background. We will clarify that this outcome aligns well with the main objective of our work. Moreover, we will follow the reviewer’s suggestion to include more representative visualization results on EventDC-Real to further highlight improvements in dynamic regions.\n\n*2. Considering the full range of datasets (i.e., Figs. 4, 5, 6, and 7(b)):*\n\nThe dynamic car in Fig. 5, the moving person and motorcycle in Fig. 6, and the person, bicycle, and car in Fig. 7(b) all demonstrate that our method consistently performs better around dynamic objects. These results strongly support that our method aligns well with the main goal of enhancing depth estimation in dynamic scenes.\n\n\n**To Weakness5, Question5, and Limitation3:**\n\nAs shown in Table 6, we ablate our EventDC by comparing its performance with (EventDC-iv) and without (EventDC-ii) the event stream. The corresponding RMSEs are 638.3 mm and 685.9 mm, respectively. This indicates **a reduction of 47.6 mm when incorporating the event stream**. The result demonstrates the effectiveness of leveraging event information.\n\nFollowing the reviewer's suggestion, we construct EventDC-x by removing only the event stream from EventDC-ix, while retaining both the EMA and LDF modules with deformable convolutions. As reported in Table R3, excluding the event stream increases the RMSE and MAE by 15.2 mm and 7.5 mm, respectively. This further confirms the contribution of the event stream in enhancing depth estimation accuracy.\n\n*Table R3: More ablations on EventDC-Real.*\n| Method       | RMSE (mm) | MAE (mm) |\n|---|:---:|:---:|\n| EventDC-ix (w/ event)  | 574.0  | 179.0 |\n| EventDC-x (w/o event) | 599.2  | 196.5 |\n\n*We thank the reviewer for this suggestion, and will include the ablation study in our final paper.*"}, {"review_id": "KtFqhusnT3", "reviewer": "Reviewer_C3TZ", "summary": "The paper introduces a new event-driven depth completion framework, named EventDC, which is designed to address the challenges of dynamic environments. The model mainly consists of two modules, Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF), to mitigate the misalignment caused by ego-motion and local depth inaccuracies due to object motion. Two dedicated loss constraints are used for supporting training. Besieds, it builds an event-based benchmark for depth completion.", "questions": "As listed in the \"Weaknesses\". I would consider raise the score if the rebuttal is satisfying.", "limitations": "None.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and formatted.\n2. In this paper's setting, the results seems outperform other method of depth completion.\n3. The EventDC benchmark is a good baseline for future researchs.\nWeakness:\n1. In real world, if there are rich and complicated motions in the environment, not only be the color image not reliable, the sparse depth is not reliable neither. In this situation, how to and why use event streams to fix or complete the unreliable depth map? \n2. The model architecture lacks novelty. In the EventDC, the EMA and LDF all consists of basic DCN blocks, which is a commonly-used module.\n3. Another key concern is the fairness of the comparison experiments. While in the motion-rich environments, the image data are designed or set to possessing blur artifacts. It may not fair for image-based methods. Are there futher experiments for demonstration?", "was_revised": true, "final_justification": "Considering the rebuttal and discussions from other reviewers, I think the paper proposes a good work for event-based vision. The benchmark and the topic is good. I raise my rating after careful consideration.", "rebuttal": "**To Weakness4:**\n\nIndeed, in real-world dynamic scenes with fast and complex motion, both RGB frames and sparse depth may become unreliable due to motion blur, temporal misalignment, or sensor noise. This is precisely the motivation behind leveraging event streams, which provide asynchronous, and high-temporal-resolution measurements that are inherently robust to fast motion and extreme lighting conditions. Event data captures pixel-level brightness changes at microsecond latency, making it resilient to motion blur and misalignment that commonly affect RGB and depth sensors. \n\nIn our framework, events are used to guide the spatial sampling of features through event-driven deformable convolutions. The offsets and modulation weights in these convolutions are derived from event features. For example, as illustrated in Figure 2, the event-centric EMA module adjusts the pixel distributions of RGB and depth features using event information. This enables more precise RGB-D alignment (see Figure 7(a)). The design remains effective even when RGB and depth inputs become unreliable. This is due to the microsecond-level temporal resolution and high motion sensitivity of event streams in dynamic environments.\n\nAlthough RGB and sparse depth provide appearance and geometric priors, events offer fine-grained motion cues that help localize trustworthy structures and correct ambiguous or missing depth information in challenging scenarios. This synergy enables our model to better recover complete and reliable depth maps under conditions where traditional sensors struggle.\n\nFinally, we would like to further clarify our current focus:\n\nOur work focuses on highly dynamic scenes where RGB-D misalignment and local depth degradation commonly occur near moving objects or during fast camera motion. Although RGB and sparse depth are generally informative in most parts of the scene, we explicitly target regions where they become less reliable due to motion blur or latency mismatch. Thus, the proposed setting does not assume that both RGB and sparse depth are globally unreliable. Instead, it accounts for their reliability being spatially and temporally variant, which is common in real-world dynamic scenarios. We thank the reviewer for this insightful question, and will add the discussion into the final paper.\n\n\n**To Weakness5:**\n\nThank you for raising this important concern. It is true that DCN is a widely used module. However, the contribution of this work does not lie in merely using DCN. Instead, it lies in how DCN is adapted and leveraged specifically for event-guided depth completion in highly dynamic scenes.\n\n**First**, both the EMA and LDF modules are carefully designed to tackle the unique challenges posed by RGB-D misalignment and unreliable depth around moving objects in dynamic environments. In particular:\n\n(a) The EMA module uses event features to dynamically generate DCN offsets and weights. This enables spatially adaptive feature alignment across RGB and depth inputs. To the best of our knowledge, this is one of the first methods proposed to use events to guide deformable alignment for depth completion in dynamic scenes.\n\n(b) The LDF module further refines depth estimations around moving objects by learning motion-aware masks from events, which is crucial in scenes with locally degraded or missing depth.\n\n**Second**, our integration with event data is notably different although DCN is a standard building block. This includes our design of the supervision and training strategy. For example, how the offset is driven by event features. In contrast to conventional DCN usage, which typically operates in a generic spatial context, our formulation is event-centric. It uses the microsecond-level temporal cues provided by event streams.\n\n**Finally**, the novelty of our work is not limited to architecture, but also includes the problem setting, i.e., event-guided depth completion in dynamic RGB-D scenes, and the benchmark contribution (EventDC dataset), which enables a more rigorous study of this underexplored problem.\n\nWe hope this explanation clarifies the novelty and motivation of our design. We will revise the manuscript to better highlight these aspects. Thank you again for the constructive feedback!\n\n\n**To Weakness6:**\n\nThank you for raising this important concern. We would like to clarify that our comparison setting is **fair** and **rigorous**. All compared methods fall under the category of depth completion, which take both color images and sparse depth as inputs, **not image alone**. Similarly, our EventDC method also takes color images and sparse depth as inputs, with the addition of event streams.\n\nOur EventDC benchmark comprises three distinct subsets, i.e., *EventDC-Real*: Real-world RGB-D-event recordings *without any synthetic motion blur*; *EventDC-SemiSyn* and *EventDC-FullSyn*: Semi-synthetic and fully synthetic datasets *with simulated motion blur* to emulate high-dynamic scenes.\n\nThis setup ensures that our evaluation covers a wide spectrum of motion conditions, from **real-world clean input** to **extreme motion-degraded cases**. In particular, EventDC-Real **does not contain any artificially induced blur**, and yet our method still outperforms image-based baselines. This provides strong evidence that our model is effective even without relying on synthetic motion artifacts.\n\nFor EventDC-SemiSyn and EventDC-FullSyn, motion blur is intentionally added to simulate fast motion scenarios. These scenarios are the core focus of our work. The goal is to benchmark the robustness of RGB-D and event-based methods under such conditions. This is not intended to bias the results against image-based methods, but to fairly reflect real-world challenges where event streams offer the greatest benefit.\n\nFurthermore, all methods are evaluated on the exact same inputs to ensure fairness in the comparison. The advantage of our model under motion blur conditions arises from its ability to leverage microsecond-resolution event data, which is inherently lacking in image-based models.\n\nAdditionally, to further support the fairness of our evaluation, we provide the following results on a version of EventDC-FullSyn where no motion blur is applied to color images:\n\n*Table R4: Results on EventDC-FullSyn without motion blur.*\n| Method | RMSE | MAE |\n|---|---|---|\n| CSPN     | 840.6 | 391.8 |\n|OGNI-DC | 657.5 | 147.6 |\n|EventDC  | 608.3 | 139.4 |\n\n*We appreciate the reviewer’s comment in helping us to improve the clarity and completeness of our work. We will add these results in the revision.*"}, {"review_id": "WCsq6adEH8", "reviewer": "Reviewer_G3qD", "summary": "This paper proposes EventDC, the first event-driven depth completion framework tailored for dynamic scenes. It addresses the common challenges of RGB-LiDAR misalignment and depth degradation caused by fast ego-motion and object motion, which conventional sensors and fusion approaches struggle to handle. The method introduces two novel modules:\n\n1. Event-Modulated Alignment (EMA): leverages event features to predict spatial offsets for RGB and LiDAR features, improving global alignment and multi-modal fusion during encoding.\n\n2. Local Depth Filtering (LDF): focuses on decoder-side refinement near moving objects, using event-informed motion masks and adaptive depth filtering.\n\nIn addition, the paper presents the first benchmark for event-based depth completion, including real-world (EventDC-Real), semi-synthetic (EventDC-SemiSyn), and synthetic (EventDC-FullSyn) datasets. Extensive experiments show consistent SOTA performance across all settings, with detailed ablation studies and complexity analysis.", "questions": "1. Use of the benchmark: Do you plan to release the simulation or preprocessing tools used to create EventDC-SemiSyn and EventDC-FullSyn? This could encourage broader community engagement.\n\n2. Failure cases: If possible, could you describe typical scenarios where EventDC might underperform (e.g., repetitive textures or extremely low motion regions)? Even a qualitative description would enhance the paper’s completeness.", "limitations": "Yes.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. Interesting problem and new benchmark datasets: This paper addresses a real yet underexplored scenario, i.e., depth completion in highly dynamic scenes, where existing RGB-D systems underperform. Furthermore, It introduces a three-part benchmark (real/semi-synthetic/full synthetic), which is a notable contribution to the field, likely to promote further research in event-based depth learning.\n\n2. Well-motivated design: The EMA and LDF modules introduce event-driven dynamic convolution for both global alignment and local refinement, exploiting events’ high temporal resolution effectively.\n\n3. Strong performance: EventDC outperforms 9+ strong baselines on three datasets with consistent improvements in many metrics without post-processing or additional priors.\n\n4. Comprehensive experiments: Detailed studies isolate the contribution of each modality (RGB, event, sparse depth), component (EMA/LDF), and design choices (dynamic convolution), which strengthens the claims. Comprehensive experiments show the superiority of the proposed method. \n\nMinor Weaknesses:\n\t\nWhile the paper is overall strong and well-executed, a few aspects could benefit from additional clarification or discussion:\n\n1. Dependence on high-quality events: As acknowledged, the method assumes reliable event streams and well-synchronized RGB-D-event input. A brief discussion on robustness to sensor noise or misalignment would improve practical insights.\n\n2. Benchmark diversity: While the EventDC benchmark is a valuable contribution, especially with a real-world component, its coverage of diverse environmental conditions (e.g., outdoor lighting, long-range scenes) could be discussed to help readers understand the scope and future extension possibilities.\n\nNone of these points undermine the core contributions, and they can be addressed through minor clarifications.", "was_revised": true, "final_justification": "I agree with Reviewer 1o7Y. This paper makes meaningful contributions by tackling the underexplored problem of event-based depth completion, effectively filling a gap in the field. I am willing to raise my score.", "rebuttal": "**Minor Weakness1:**\n\nAlthough our method assumes reasonably synchronized input, it is explicitly designed to handle the challenges of imperfect RGB-D alignment in high-speed or dynamic scenes. In such cases, event data which is naturally asynchronous and high-temporal-resolution, plays a key role in correcting spatial misalignments and compensating for motion blur or sparse/degraded depth near dynamic objects.\n\nTo this end, we introduce event-guided deformable convolution, where the event stream dynamically generates the offset fields and modulation weights. This allows the model to adaptively sample features from more accurate spatial positions, even in the presence of RGB-D misalignment. In other words, instead of requiring perfectly aligned input, our method leverages the asynchronous nature of events to mitigate such issues.\n\nWe will add a discussion of this point in the revision, to clarify that our approach is particularly beneficial in non-ideal, high-motion scenarios, and offering future directions such as noise-aware offset estimation or sensor calibration refinement for further robustness. Thank you for the insightful suggestion.\n\n**Minor Weakness2:**\n  \nWe would like to emphasize that the EventDC benchmark already includes a diverse set of environmental conditions to facilitate comprehensive evaluation. Specifically:\n\n*1. Scene diversity:* Our dataset covers both indoor and outdoor scenarios, ranging from confined corridors to large open areas.\n\n*2. Lighting variation:* We include sequences captured in daytime, nighttime, and low-light conditions, making the benchmark suitable for evaluating models under challenging illumination.\n\n*3. Depth range:* The effective depth spans from approximately 0.1 meters to 100 meters, thus covering both close-range tasks (e.g., indoor navigation or robotic grasping) and long-range perception (e.g., outdoor driving or surveillance).\n\n**Question1:**\n\nYes, we will release the proposed datasets upon paper acceptance. In addition, we are glad to share the corresponding preprocessing tools to further support and benefit the research community.\n\n**Question2:**\n\nWe agree that discussing failure cases can provide a more complete understanding of our method's limitations. EventDC, while effective in high-motion scenarios, may underperform in the following typical cases:\n\n*1. Extremely low-motion or static regions:* Since event data is inherently triggered by intensity changes, regions with little or no motion (e.g., static background or slowly moving objects) generate very few events. This limits the contribution of event-guided modules in such areas, making the model rely primarily on RGB and sparse depth.\n\n*2. Repetitive or low-texture areas:* In scenes with repetitive patterns or low texture (e.g., blank walls or road surfaces), events may be ambiguous or noisy, leading to inaccurate offset estimation in deformable convolutions.\n\n*3. Sensor artifacts or miscalibration:* Although our method is designed to handle RGB-D misalignment, large-scale calibration errors or noisy depth input may still affect performance, especially in complex outdoor scenes.\n\n*Thank you again for these valuable comments. We will carefully consider incorporating these discussions in the revision.*"}]

Author rebuttal:

Meta-review: This paper presents a novel event-driven dynamic scene depth completion pipeline which effectively predicts dense depth from event camera-based sparse measurements in highly dynamic environments. 

Strengths:
1. The first work for event-driven dynamic scene depth completion.
2. Good EventDC benchmark and three useful datasets.

Weaknesses:
1. Some writing issues, such as image layout and inconsistent formula symbols.
2. Lack of explanation of dynamic backgrounds process and different deformable convolution weight designs in encoder and decoder.
3. Lack of latency analysis data

After rebuttal, the final rates are 3 Borderline accepts and 1 Strong Accept. The reviewer's doubts and questions were well addressed in the rebuttal process, and received positive comments and recognition. Given these positive reviews, the AC recommends acceptance and suggests the authors to fix some formatting issues based on the reviewers' comments.
Decision: Accept (poster)