Dense Metric Depth Estimation via Event-based Differential Focus Volume Prompting
Authors: Boyu Li
Peiqi Duan
Zhaojun Huang
Xinyu Zhou
Yifei Xia
Boxin Shi
Venue: NeurIPS 2025 poster ()
Keywords: Event Camera
Depth Estimation
Focus Volume
Primary area: applications

Abstract: Dense metric depth estimation has witnessed great developments in recent years. While single-image-based methods have demonstrated commendable performance in certain circumstances, they may encounter challenges regarding scale ambiguities and visual illusions in real world. Traditional depth-from-focus methods are constrained by low sampling rates during data acquisition. In this paper, we introduce a novel approach to enhance dense metric depth estimation by fusing events with image foundation models via a prompting approach. Specifically, we build Event-based Differential Focus Volumes (EDFV) using events triggered through focus sweeping, which are subsequently transformed into sparse metric depth maps. These maps are then utilized for prompting dense depth estimation via our proposed Event-based Depth Prompting Network. We further construct synthetic and real-captured datasets to facilitate the training and evaluation of both frame-based and event-based methods. Quantitative and qualitative results, including both in-domain and zero-shot experiments, demonstrate the superior performance of our method compared to existing approaches. Code and data will be available at https://github.com/liboyu02/EDFV/.

Dense Metric Depth Estimation via Event-based Differential Focus Volume Prompting
Boyu Li Peiqi Dua Zhaojun Huang!.2 Xinyu Zhou² Yifei Xia 1.2 Boxin Shi 1,2 Sttae Key Lab of Multimedia Info. Processing, School of Computer Science, Peking University 2 Natioona Eng. Research Center of Visual Tech., School Computer Science, Peking University 3 State Key Lab of General AI, School of Intelligence Science and Technology, Peking University {1 (liboyu, duanqi0001. huangzhao zhouxiny yfxia, shiboxin)@pku edu.
Abstract
Dense metric depth estimation has witnessed great developments in recent years. While single-image-basel methods have demonstrated commendable performance in certain circumstances, they may encounter challenges regarding scale ambi- guities and visual illusions in real world. Traditional ceeth-frrom-foce methods are constrained by low sampling rates during data acquisition. In this paper, we introduce a novel approach to enhance dense metric depth estimation by fusing events with image foundation models via prompting approach. Specifically, build Event-based Differential Focus Volumes (EDFV) using events triggered through focus sweeping, which are subsequently transformed into sparse metric depth maps. These maps are then utilized for prompting dense depth estimation via our proposed Event-based Depth Prompting Network. We further construct synthetic and real-captured datasets to facilitate the training and evaluation of both frame- based and event-basee methods. Quantitative and qualitative results, including both in-domain and zero-shot experiments. demonstrate the superior performance of our method compared to existing approaches. Code and data will be available at ttttpp://githuue www.cooommo/.. comm/iioyuu2/EDDFF
Introduction
Dense metric depth estimation an important task predict a dense map with absolute depth values of valid pixels. It plays a significant role in downstream applications such augmented reality (25 and virtual reality [27]. Early single-image-base
methods use hand-crafted features (56) or simple regression networks [12,, but are limited by their robustness and generality. Recently, Image Foundation Models (IFM) trained on large datasets for depth estimation [3] 67 have been
proposed. They are good at producing shapes and relative layouts of objects, but may suffer from scale ambiguities and visual illusions due the lack of sufficient scene prior [33]. Although several
studies (23 48 5 have incorporated camera intrinsic parameters to mitigate ambiguities for metric depth estimation, they may nonetheless encounter inaccuracies under certain conditions (Fig. (c)).
For more accurate metric depth estimation, various cues have been utilized such as another view (stereo) 34, LiDAR measurements (57 45 and focus/defocus 40 Among them, stereo
matching may have difficulties in handling spatial misalignments between frames 63 LiDAR needs actively emitted laser pulses, and thus may pose problems to privacy and environment 44 Depth from focus/defocus (DFF/DFD) methods rely on the fact that during focus sweeping, there is an optimal focusing timestamp for each point of the scene, where the Circle of Confusion (CoC) the smallest (Fig. (d)) 40). They do not require auxiliary movements or equipment, thus ensuring
Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Event-RG8 hybrid system
(b) Input
(c) Output
[d) Illustration
Event camera
Focus
sweeping
Object
CoC
Image plane
Triggering events
GT Depth
Metric3D V2
Lens
swesping
Value
Timey
Calimall
FV
Torusing
Beam splitter
Single image
DFF-DFV
Ours
timestainp
Figure (a) We introduce pipeline using both all-in-focus (AIF) images and focus sweeping triggered events for depth estimation. The input data can be captured with an event-RGB hybrid system. (b) Inputs of our method. (c) Comparison with other methods (single-image-bass
Metric3D V2 23 and DFF method DFF-DFV (64). (d) The illustration of the pinhole camera imaging model (top) and our proposed EDFV (bottom). Compared with frame-based FV and DFV, our method could better find the optimal focusing timestamp, resulting in more accurate depth estimation.
minimal spatial misalignment and applicable most scenarios [64]. Most of them use Focus Volumes (FVs) and Differential Focus Volumes (DFVs) to assist depth estimation [59] 62]. However, at least 4 frames are usually required to achieve desirable results [40, increasing data acquisition difficulties, especially the consumption of operation time.
Event cameras are neuromorphic sensors inspired by human retina 31 The high temporal resolution enables them to record many temporal bins of the focus sweeping process in a short time, They have been employed in image restoration tasks such as deblurring [69], super-resolution (28), and all-in-focus (AIF) image reconstruction [38]. Recently, they have also been leveraged for sparse depth estimation (26). These methods utilize event cameras to get better results with a shorter capture time. However, the sparsity of events makes it hard to get dense predictions. In contrast, DFF methods could extract dense information from images. These complementary characteristies motivate us to think: Can we fuse the advaniages of both images and events for dense metric depth estimation?
There have been previous works achieving depth completion by prompting IFMs with sparse ob- However, utilizing event cameras for dense metric depth estimation still faces several challenges: (1) To better use the focus information encoded in events, a more efficient representation tailored for depth estimation is needed. (2) As events provide different sparse depth patterns from other sensors such as LiDAR because they mainly trigger at boundaries textures, directly applying existing prompting strategies may fail to fully exploit their unique advantages and thus yield suboptimal performance. (3) No public datasets with ground truth (GT) depth, image focal stacks, and events are currently available.
servations [45] 33
In this paper, we propose a pipeline combine events with images (Fig. 1 (a) & (b)) for more accurate dense metric depth estimation. We propose a new representation called Event-based Differential Focus Volume (EDFV) to guide sparse depth estimation. As shown in Fig. (d), the high temporal resolution of events enables us to find the optimal focusing timestamp more accurately. Then we use EDFV to prompt the initial dense estimation results from IFM with our Event-based Depth Prompting Network (EDPN) for the final prediction. To evaluate both frame-based and event-based DFF methods in unified reliable datasets, we synthesize two datasets in two different ways, and also capture datasets with real events. Above all, our contributions are as follows:
We propose novel representation EDFV constructed from events generated during focus sweeping for event-based sparse depth estimation, which accounts for the event triggering mechanism and enables more effective extraction of focus/defocus information.
We design a prompting strategy to employ both EDFV and AIF images for dense metric depth estimation. The pipeline first predicts sparse depth maps from EDFV, and then leverages them to prompt dense depth estimation with IFMs through our prompting network.
We construct two synthetic datasets including AIF images, focal stacks, events, and GT depth for the training and evaluation of both Frame-based and event-based DFF methods. Besides, semi-real dataset and a real dataset with challenging real-world scenarios are also captured for evaluation only. These datasets will be made public to facilitate future research.
We achieve at most 29.1% improvement in root mean square error (RMSE) on our proposed dataset compared with existing sangle-imaee-basee and traditional DFF methods.

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2
Related work
Single image depth estimation. Traditional methods (20 [56 use hand-crafted operators to estimate depth maps from single images. These methods usually fail to predict accurate depth in textureless areas. The learning-basel approach learns deep depth cues from training data, thereby reducing the estimation error on similar test data 22 70 However, they still struggle with zero-shot estimation in unseen scenes. With the rise of foundation models, some methods B 67 have used massive training data to achieve better ambiguity problem, more recent methods [68 23 48, 47 16 take camera intrinsic parameters as additional inputs. But they still face challenges in real world due to limited scene prior, as shown in Fig. (c). We seek to address the problem by prompting IFMS with information from focus/defocus.
relative depth estimation results. To solve the scale
Focus measures and depth from focus/defocus. The depth estimation methods based on fo- cus/defocus take advantage of the lens effect that "the more the scene is away from the focus depth, the blurred in the image becomes" to realize the task of depth estimation from images [40). Therefore, the focus measure operators they use for evaluating the "in-focus" degree of each pixel have great influence on their performance [46]. Early researchers employ hand-crafted operators, such as gradient-based operators (15). wavelet-based operators [65], and discrete- cosine-transto
based operators [30] for the evaluation. Some methods estimate the depth map directly based on the blurred degree of local patches 43 4T, but the foreground/backgroon
ambiguity and the influence of the
occlusion scene have an impact on the performance [55]
Deep learning methods are introduced to replace hand-crafted operators and improve performance 18 40 62]. DFV (64) is proposed to better utilize information by computing the first-order derivative on the stacked features. A more recent work [33 improves zero-shot performance by considering camera parameters in advance. Recently, HybridDepth [14] tries to incorporate both pretrained traditional DFF method and pretrained IFM as relative depth estimator. However, these methods meet problems such as the dependence on focal stack numbers. We introduce events to record the focus sweeping process and take advantage of high speed to break through the above bottleneck.
Event-guided depth estimation. Event-based depth estimation mainly falls into three categories: monocular, stereo, and DFF. Early optimization- based monocular methods 22 [T only output sparse or semi-dense results. Learning-bai methods [19 have shown promising dense results. Although claimed as "monocular", these methods need camera or object motion during capture process, thus causing possible misalignment. Besides, the motion patterns may influence the quality of triggered events, thus affecting the estimation accuracy 35 Stereo methods include event-based symmetric stereo 61 42 and event-intensity asymmetric stereo 71
7 foundation models from image domain to improve the generality. For event-based DFF methods, previous work (26) only predicts sparse depth from pure events. A more recent work [22] leverages existing grayscale video reconstruction approaches for depth estimation. Their method lacks the input image modality, which could constrain their zero-shot performance. contrast, we aim to predict dense maps and improve seot performance by fusing information from IFMs.
Recently, some works g 37 employ
3 Preliminaries
In this section, we give brief review of frame- Focus Volume and Differential Focus Volume in Sec. 3.1. and formulate event-based Differential Focus Volume in Sec. Sec.3.2
3.1 Frame-based Focus Volume
According to the pinhole camera imaging model shown in Fig. (d). objects away from the lens focus depth F will exhibit defocus blur in the image (3). The defocused image can be modeled as the result of the convolution operation between AIF image I and spatial-variant Point Spread Function (PSF). The shape and blur degree of PSF are relevant to 0, i.e., radius of CoC, which is derived as:
0-A- ((I,v) -
(I)
D(I,y)
F-f
where A is the diameter of the lens aperture, D(x, y) represents the depth of the scene corresponding to pixel (z, y), and ff is the focal length. According to Eq. I the closer F to D(x,, 3). the smaller
3

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EDFV Encoder
EDFV Decoder
SparseNet
EDFV
ppprss
Sparse depth
Events
SAME
Eeaach
MCPM
Edense
GT depth
Our depth
Image Foundation
Model
Frozen
DenseNet
Trainable
Image
Multiplication
Initial dense depth
EDPN
Figure 2: The pipeline our method. Firstly, an image is fed into the IFM for an initial dense depth map. Concurrently, events are fed into an encoder to build multi-scale EDFVs. The SAME strategy employed to extract spatial attention masks from images and events, which are multiplied with the output of the EDFV decoder to generate a sparse depth map. The two maps are subsequently fed into EDPN, where features are extracted from sparse and initial dense depth by SparseNet and DenseNet, respectively. Finally, the features are fused with MCPM to output depth. Caparse is calculated for depth consistency between sparse depth and masked GT (GT multiplied with the same spatial mask), Ldense between final depth and GT, well as patch for multi-scale local consistency.
0 will be. When F = D(x, y). we have = 0, which means the unique minimum point of A corresponds to the depth of the object.
Frame-based DFF methods take images at N different focal depths Fi = 1, I,....N.. and find the optimal focused depth of each object in the scene. common method is use FV to store the "in- focus" degree of each pixel in each image. As discussed in Sec. 2 the focus measures can be divided into hand-crafted operators and deep-learning methods. Traditional methods find the maximum focus score among these FVs, while learning-basee methods [62 13 calculate the probability scores for the actual depth falling into each of these depths, and multiply them with these depths to get the result. Learning- based methods usually have better results. However, the feature magnitude of the sharpest pixel may be salient in some circumstances due to noises and imperfect measurements [64]. Taking the single extremum into consideration. if taking the difference in the features, the polarity of the difference should be reversed around the best focal depth, and the sharpest pixel will be more significant to distinguish. This formulates the so-called DFV.
3.2 Event-based Differential Focus Volume
Event cameras bio-inspired sensors that record intensity changes in the scene in asynchronous manner. the log-scale intensity of a pixel [( y. t) change exceeds a preset threshold c at timestamp an event (æ, y. p) will be triggered, where p {+1, -1} denotes the polarity indicating whether increasing or decreasing. This process can be formulated as:
+1, log[I(x, y, ()) log(I(x y, y, At) 6,
1, log(I y. ()) log(I y, (- A() < -t.
Events triggered around the intensity-changing pixels at boundaries between objects of image may experience polarity reversal before and after focusing.
Observation 1:
Here, the 'intensity-changing pixels" mean pixels where themselves and the surrounding pixels do not have the same intensity. We assume simple scene where there are only two objects, one in the foreground and one in the background, both with uniform intensity and depth. During the focus sweeping process, the intensity of pixel (I, y) on the background any timestamp can be formulated as:
I(x, v) = I, + I,P((t)
where I, and , intensities of the background and foreground, respectively, and Pr is the value of the foreground PSF on pixel (z, y). Then the log-scale intensity change is:
Is + (t) A log(I(z, log I, P,P(t At)
Pt(t) Pi((t-A1 (4)
log(1 + I, I, + I,P((t -A))
4

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From the above equation, we derive:
sign( A log([( t))) (ig)(AP:(t)),
(5)
where sign(> (-) denotes the mathematical sign function.
Observation 2: The sign of the right side of Eq. (5) will change before and after focusing.
Observation If the intensity-changinn pixel is on the texture of single object instead of an edge separating two objects, the triggered events around it may also experience a polarity reversal.
The derivation of these observations can be found in the Appendix Sec. Based on the above observations, focus-sweeping triggered events inherently encode differential "in-focus" information. We exploit this characteristic construct deep EDFV from events. Similar to DFV, EDFV may not perform well in textureless regions lacking salient intensity changes during sweeping, Moreover, the derivation shows that background events may reflect foreground intensity, differing from DFV. We will show how to address these challenges in our pipeline.
4 Method
The pipeline of method shown in Fig. 2
We first estimate initial dense depth map from frozen IFM. Concurrently, we build EDFV with an encoder and predict a sparse metric depth map from events. The final estimation is generated by feeding two maps into our prompting network EDPN.
4.1 Initial dense depth estimation
Recent IFMs for depth estimation [66 67 take a single image as input, and output dense depth map. Although maybe scale-ambiguous, they could preserve object boundaries and extract important semantic information of the scene. Therefore, we feed the image to IFM to estimate an initial dense depth map. In this work, choose Depth Anything (DA) V2 [67] our baseline model.
4.2 EDFV construction
In order use the depth information encoded in events, simple way could involve directly processing events with hand-crafted operators, such as the polarity changing timestamp for each pixel. However, this approach suffers from high noise sensitivity and may output satisfactory results. Similar to deep frame-based FV and DFV discussed in Sec. 3.1 and the design of event-based neural representations [60], we first stack the events into N temporal bins with their polarities, and then transform them into EDFV with multi-scale convolutional encoder.
Previous studies 62 show the frame number of discrete focal stacks N influences the performance of FV and DFV methods. But thanks to the high temporal resolution of events, when we sweep the focus ring, they can record continuous intensity changes for each pixel. Therefore, we can theoretically generate any number of event bins. Here we choose N = 32, and an ablation study of this number is provided in Sec. 5.5 Besides, we select the central depth of each bin as the candidate depths.
4.3 Sparse depth estimation
To solve the challenge that EDFV of a pixel may reflect depth values of surrounding pixels, we adopt multi-scale decoder with residual convolution blocks to process the EDFV and efficiently estimate the sparse depth map. However, as the network itself could not identify effective pixels, noises of events in textureless areas may be scattered through these operations. To tackle this issue, propose a Spatial Attention Mask Extraction (SAME) strategy to force the network to concentrate on the effective areas of events. Specifically, spatial attention mask M, which multiplied with the output EDFV decoder to get a sparse depth map. Besides, add dilation operations M to solve possible misalignments, which is:
image gradients VI and event density Pe to build
N
M = Dilatee(VI>>e)) er) >0)>)))), D. = M. Σ SEDDFV) Ft),
(6)
where e and 6, are adjustable thresholds, S, the normalized probability score, and D. is the sparse depth map. The choice of and e, would affect the effective regions of events, and thus affect
5

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Sparse event features
1/8
1/4
1/2
Deform
C
Gone
Residual Conv
Fusion Block (FB)
Cony
Conv Cross Altention Block (CAB)
ReckQual Conv Unit
Upsample
Project
FB
image (a)
(b)
(c)
Figure 3: (a) The structure of MCPM, with CABs and FBs of different scales. (b) The detailed structure of CAB. (c) The detailed structure of FB.
final predictions. In order to improve the robustness of our network for the non-uniform and inconsistent event triggering thresholds and various noise levels real world, we randomly set these parameters during our training, and select fixed set of them for evaluation.
To provide flatter distribution, we use softplus instead of softmax normalize the scores, which is:
In(1 + exp(S.))
S,
(7)
Σラ=1 In(1 exp(S,))
where S; is the non-normalized score. Compared with softmax, this normalization improves depth estimation by interpolating among candidate depths 62
4.4 Event- depth prompting
Our prompting network leverages scale cues from event-based sparse depth to prompt the initial dense depth map for final results. Previous works 45 33) mainly focus on LiDAR prompting approach, which differs in modality from event data sensitive to edges and textures. To bridge this gap, employ two U-Net-based (53) networks, i..., SparseNet and DenseNet, to project sparse and initial dense depth maps into unified feature embedding space. Considering their different characteristics, we add max max-poot -ooolonoiig layers to SparseNet to better densify the sparse information. In contrast, we add large kernel convolution layers DenseNet for extracting more high-level features of the dense depth map. We collect features from decoders of different scales of SparseNet and DenseNet.
In order solve the prompting challenges between two modalities, we further design Multi-scale Cross-attention attention--uiied guided Prompting Module (MCPM, as shown in Fig. 3). It consists of Cross Attention Blocks (CAB) and Fusion Blocks (FB) working at different scales. CAB first uses convolution layers to compute queries Q from dense image features F,, K and V from sparse event features F.. In order handle the sparsity of events and possible misalignments between event and image features caused by motion as well as the challenge discussed in Sec. 3.22 we employ deformable convolutions [10 to generate K and V. Then CAB use attention map as the matching score of Q and K, and then use V to fill the output result. The above process can be formulated as:
QK VV)
Q = Conv ConvooF)).FF K, DDeoormCov (F.), DCAB = Conv(Softmax
(8)
where DCAB is the output depth map of CAB, and is a learnable scaling parameter.
Furthermore, we refine the output of CABs in multi-scale way using FB. FB consists of Residual Convolution Units (2], and progressively upsample the representation by factor of two in each fusion stage. The final output depth spatial resolution is the same as the input image.
4.5 Loss functions
We supervise our final results with pixel-wise MSE loss between final output depth D and GT D gt:
Ldense = E
D
Detll 1
In order to constrain our predicted sparse depth map from events to be accurate, we multiply the GT depth with M, and calculate an MSE loss with sparse depth D,;

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sparse D Dg: MI]²].
(10)
Besides, according to previous studies 477. patch-level edge-guided guided scale-shift invariant loss could help improve the local precision of models. Therefore, we adopt a multi-scale version formulated as:
Σ Br (D( - No (D 1=0
Cpazch
(11)
where L = 3 corresponds to the level of MCPM, P1 are weighting parameters, Q2 is the set of selected image patches, No is the standardization operation over patch w, CAB are the output of CAB at level within w, and D D are the corresponding downsampled GT. / indicates original resolution. gt
L.w
The final loss function is the linear combination as:
[
diense + aLsparse + 7 Paatchs
(12)
where and y are parameters for balancing the contribution of different terms.
Experiments
5.1 Datasets
Blender-Syn. We follow DefocusNet [40 to use Blender to render our first synthetic dataset. We put random objects in the scene, and wrap their surfaces with textures sampled from Poly Haven 49 to add their reality. For each scene, we shuffle and rescale the objects, and sweep the focus depth of the camera from 0.1 m to 30 m to generate focus sweeping video of 500 frames, with an aperture f/1.4 and a focal length 100 mm. We select 150 scenes as training data and 60 scenes as test data, containing AIF images, GT depth maps, events and focal stacks. For each focal stack, we render 500 frames with different focal depths, and use them to simulate events using V2E [24] Besides, to simulate possible motion in real data capturing, we add small position movements to each object.
Sintel-Dr: Bokeh. order to overcome the limitation of object shapes and depth diversitics in our first dataset, we another large-scale single image depth estimation dataset, i.e., Sintel [6], and generate focal stacks by adding synthetic blur to it. Our training set contains 130 scenes and test set contains 30 scenes. We use the bokeh rendering model Dr. Bokeh [58] synthetically bokeh render these images at different focal depths. The blur strength parameter of Dr. Bokeh is set to 30 for more salient defocus cues. The biggest lens kernel size is 71, and the gamma parameter 2.2. The event simulation process is the same as Blender-Syn.
We further capture semi-real dataset using 4D Light Field Dataset [2] as the source. It consists of 24 realistic scenes with densely sampled light field data and GT depth. After constructing focal stacks and AIF images from original data, we stack the frames into a video with 50 FPS to simulate a focus-sweeping process, and display it on the screen. Then we use a Prophesee EVK4 event camera capture real events by shooting the screen.
WOLFD-Semi-Res
EDFV-Real. We further capture real-world test dataset with AIF images, focal stacks and events for frame-basee and event-based methods. The hybrid camera system we use consists of a machine vision camera (HIK VISION MV-CA050-12UCE and an event camera (Prophesee EVK4). The two cameras are co-aligned with beam splitter. Both cameras use one lens with focal length of 16 mm VHIKVISION MVL-MFI (1F22---MPP)) Since this lens did not exhibit noticeable FoV breathing during our observations, we did not account for this effect data acquisition. Besides, we use checkboards to calculate the homography matrix ensure alignment between these two cameras. We capture AIF images with aperture f/16, focal stacks with f/8, and events with f/2.4.
5.2 Experimental settings
Metrics. The evaluated metrics include RMSE (root mean square error), RMSE log (root mean square logarithmic error), AbsRel (absolute relative error: Id dl/d), log10 (absolute log 10 error,
7

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Table 1: Quantitative comparisons of in-domain metric depth estimation. 1 (4) indicates the higher (lower), the better performance. The best performances are highlighted bold, and the second best in underline. Left numbers denote results Blender-Syn, and right numbers denote Sintel-Dr. Bokeh.
Sye
Sintel-Dr. Bokeh
Method
Type
RMSF()
AhuRel(1)
log(O(L)
6x(1)
6x(T)
dx(T)
RMSF(
AhwRel(.)
leg10(1)
6:(7)
(x(")
Ax(T]
DefocusNet
DFF
0.241
0.372
0.100
0.734
01818
0.861
0.2139
01.728
0.192
0.412
0.644
0.797
DFF-PV
DFF
0.184
0.223
0.062
0.862
0.907
0.160
0.661
0.109
0.266
0.863
0.898
0.871
DDFS
DFF
0.244
0.357
0.109
0.723
0.804
0.349
0.282
1.072
0.282
0.461
0.578
0.648
DephidDDephh
DFF
0.089
0.125
0
0.825
0.925
0.999
0.273
0.657
0.295
0.253
0.393
0.540
DA V2
Mono
0.063
0.089
0.035
0.865
0.956
0.989
0.297
0.482
0.361
0.350
0,419
0.472
Metric3D
0.179
0.561
DAC
Mono
0.176
0.238
0.115
0.651
0.868
0.917
0.273
0.95
0.289
0.268
0.409
0.573
Ours
DFF
0.068
0.077
0.028
0.919
0.972
0.987
0.095
0.141
0.072
0.806
0.901
0.945
Image
DefocusNet
DFF-FV
DFF-DFV
HybridDepth
Metric3D
DAC
Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybridDepth
Events
DA V2
Metric3D V2
DAC
Ours
GT
Figure 4: Qualitative in-domain experiment results on Blender-Syn (top 2 rows) and Sintel-Dr. Bokeh (bottom rows) with inputs. We compare with DefocusNet [4)] DFF-FV 64
DFF-DFV 64
DDFS 13,
HybridDepth [4 and DA V2 67), Metric3D V2 23 and DAC
16
lg(d)) Ig(d)), and 6 (percentage of max(d/d, d/d) < 1.25', = 1,),).. For all the datasets, we select 5 frames with uniformly split focal depths as the input of frame-basee DFF methods for training and evaluation, which is a common number used in these works.
method using the Pytorch framework and run single NVIDIA GeForce RTX 4090 GPU. We use AdamW [36] optimizer in the training phrase. For each dataset, train for 700 epochs with initial learning rate 3 10-4 and weight decay 1 10-5 We randomly crop the input images into 320 x 640 resolution with random flipping afterwards. For loss functions, Blender-Syn dataset and = for Sintel-Dr. Bokeh.
Training details. We implement
a 0.1, 8n 1,
82 A3 0.1 in all our experiments, 7 5 for
5.3 In-domain experiments
We report in-domain results the first two datasets. For single-image-based methods, we compare with finctuned DA V2 from pretrained HyperSIM [52) VIT-L checkpoint, Metric3D V2 (23) from pretrained ViT-L checkpoint, and Depth Any Camera (DAC)
16) from pretrained outdoor NesNet101
checkpoint. For DFF methods, we evaluate DefocusNet 40) DFF-DFV (64,, DDFS 1B, and HybridDepth 14.. DFF-DFV's non-differentiable variant is denoted as DFF-FV. For fair comparison, all methods except DDFS are trained for the same epochs with their official code on our dataset. Since DDFS lacks public training code, we finetuned their released model using in-house code. Our table results use the DA checkpoint pretrained on relative depth with a VIT-L encoder.
The quantitative results are shown in Table where we denote DFF methods "DFF" and monocular methods as "Mono". Our method achieves the best performance almost all metrics. should be noted that DA V2 achieves commendable results on Blender-Syn, which may be attributed to

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Table 2: Quantitative comparisons of zero-shot metric depth estimation.
Method
Type
Blender-Syn
Sintel-Dr. Bokeh
RMSB(1)
RMSE log(()
log10(L)
6,(1)
6y(1)
6xcT)
RMSE()
RMSE
lug10(j)
DefocusNet
DFF
0.425
0,783
0.292
0.135
0.391
0.608
0.518
1.304
0.585
0.123
0.204
0.275
DFF-FV
DFF
0.325
0.666
0.162
0.669
0.732
0.775
0.267
0.982
0.143
0.177
0.364
0.518
DEF-DFV
DFF
0.369
0,710
0.196
0.651
0.551
0.707
0.270
1.038
0.366
0.192
0.332
0.495
DDFS
DFF
0.495
1.120
0.377
0.287
0.361
0.488
0.706
1852
0.726
0.200
0.331
0.251
HyhnndDopth
DFF
0.622
1,461
0,570
0.050
0.127
0.227
0.442
1,391
0.262
0.225
0.782
0.018
0.108
0,91
MetricD
0.535
0.307
0.625
0.777
0.523
0.66
DAC
Mono
0.652
1.485
0.594
0.075
0.142
0.19%
0.515
1.474
0.581
0.144
0.228
0.285
Ours
DFF
0,148
0.282
0.081
0.697
0.878
0.944
0.233
0.685
0.253
0.333
0.466
0.560
Scene
Image
Events
AiFDepthNet
DFF-FV
DFF-DFV
V2
Metric3D V2
Ours
Figure 5: Qualitative zero-shot experiment results on EDFV-Real dataset. We visualize inverse depth here. The first column is the scene overview, and the hand in the second row is just to indicate the thin paper. The numbers label the objects in each row. We compare with DFF methods AiFDepthNett 64 and single-image-basee DA V2 67 and Metric3D V2
62. DFF-FV and DFF-DFV
23
relatively simple shapes and scales. In contrast, Sintel-Dr. Bokeh contains more diverse scenes, so DFF methods may perform better. But our method still outperforms the best DFF method (DFF-DFV) in RMSE by over 29. 1% on this dataset. Qualitative results are shown Fig. 4 All the depth maps are clipped according to the depth range of focal stacks. It can be found that DFF methods are likely to produce artifacts due to low sampling rate and limited model capacity, Meanwhile, IFMs are good at predicting object boundaries but may fail to output accurate values due to scale ambiguity. Our prompting approach exhibits fewer artifacts and more accurate values.
5.4 Zero-shot experiments
We compare the zero-shot performance on Blender-Syn dataset with the model trained or finetuned on Sintel-Dr. Bokeh, and Sintel-Dr. Bokeh with models trained on Blender-Syn. Quantitative results are shown in Table 2 which verify our model's superior zero-shot performance.
We further conduct zero-shot qualitative evaluations on 4DL.FD-Semi-Real and EDFV-Real datasets. One example from 4DLFD-Seme S-o---Smi-Reaal is shown in Fig. [ with more in the Appendix Sec. D Results on EDFV-Real are in Fig. 5 As other methods trained
our data perform poorly on these datasets, we use their official pretraindd models. Our results use the Blender-Syn trained checkpoint. We designed our dataset to challenge
waggle-image-berre based methods primarily in three ways: (1) Warped textures printed on thin paper to confuse singular and gradient depth. In the first and second rows, object ① exemplifies such cases, where sengle-image-brrr based methods incorrectly predict gradient depths, while DFF--baeed and our method recover correct depth. Conversely, object @ in the first row is real object for which single-image-base -baee-aaad-ddddd and methods predict gradient depths. (2) Scale ambiguity via placement of similar-looking objects at varying depths. In the second row, objects @ @ and @ differ in depth; only our method identifies them correctly. (3) Occlusion and overlap for mislcading absolute metric scales. In the third row, objects @ and @ are distant objects (carport and building); traditional DFF methods fail due complex textures. DA V2 estimates similar depths for both, Metric3D V2 errs on the sky region (object @), while ours yields a plausible depth map.
5.5
Ablation studies
Results of ablation studies are in Table 3 All of the models are trained and tested on Blender-Syn.

---

Prompting an IFM. We show the effectiveness of prompting by the following experiments: removing IFM and directly feeding the image DenseNet for feature extraction (denoted "w/o IFM"); removing events images from input (denoted as "w/o events" and "w/o image", respectively). The results show their performances drop as expected, among which "w/o image" has the most decline.
Prompting network design. The effectiveness of our proposed MCPM is proved by replacing with simple add and convolution operations for fusion (denoted as "w/o MCPM"). Furthermore, show the effectiveness of Catch by removing it from our loss functions (denoted as "w/o Lpatch" "). Besides, conduct ablation study where replace the event inputs in method with 32-frame focal stacks, denoted as "Ours-FS". Although this brings minor improvements to some metrics, brings much more computational complexity the whole pipeline (please refer the Appendix Sec. C). which is not desirable for sesource-limitee devices.
Temporal bin number. We conduct ablation studies on the temporal bin number N. We de- note our full model of bin number as "Full- abin" (i = 4, 8, 16). As expected, the perfor- mance falls as N decreases. Besides, the per- formance gain shrinks as the temporal bin num- ber increases, which is consistent with observa- tions wraee DFF methods. Notably, our "Full-4bin" model still defeats some DFF meth- ods with 5 input frames in some metrics (refer to Table DT. demonstrating our method's advantage. We choose N = 32 in our final model.
Table 3: Quantitative results of ablation studies.
Method
RMSE())
AbsRel(
81(1)
w/o IFM w/o events
0.122
0.177
0.828
0.106
0.21
0.741
w/o image
0.216
0.381
0.579
Ours-FS
0.067
0.079
0.920
w/o MCPM 0.104
0.114
0.840
w/o Cpatch
0.073
0.096
0.874
Full-4bin
0.162
0.446
0.705
Full-8bin
0.084
0.091
0.880
Alternative IFM checkpoints. As method supports flexible deployment with other IFM al- ternatives, we further evaluate the impact of IFM model checkpoint to our method. "Ours-DA-S" and "Ours-DA-B" denote model with the DA V2 67] checkpoint of ViT-Small encoder and ViT-Base encoder [50], respectively. In addi- tion, "Ours-M3D-S", "Ours-M3D-L" and "Ours- M3D-G" denote our model with the Metric3D V2 23 checkpoint of ViT-Small, ViT-Large and ViT-Giant backbones, respectively. Our fi- nal version is based on DA V2 of ViT-Large backbone. Although the performance based on smaller checkpoints may degrade slightly, they still defeat most of the other methods (refer Table We also conduct an ablation study by excluding the influence of pretrained checkpoints, denoted as "w/o pretrain", where we train our method from scratch. The results show that even without any pretrained checkpoint, our method still outperforms most existing DFF methods in most metrics.
Full-16bin
0.071
0.083
0.909
Ours-DA-S
0.086
0.098
0.900
Ours-DA-B
0,076
0,086
0,905
Ours-M3D-S
0.122
0.143
0.813
Ours-M3D-L
0.077
0.096
0.872
Ours-M3D-G
0.085
0.103
0.855
w/o pretrain
0.126
0.223
0.864
Ours
0.068
0.077
0.919
6 Conclusion
We propose pipeline using focus sweeping triggered events and images to generate dense metric depth. Thanks to the high temporal resolution of events, we could build EDFV and get sparse depth map. Then we could get a more accurate dense estimation via prompting IFMs through our prompting network. Experiments show great in-domain and zero-shot performance our method.
Limitations. Although we have addressed potential minor misalignments and motion issues, we intend to explore alternative strategies, such as employing liquid lenses [39] adjust focus depth in future research. Besides, while our method demonstrates excellent pero-shot performance with current training datasets, scaling training data akin to IFMs could improve outcomes. Additionally, in low-light or highly-smooth scenarios, our method may suffer from low-quality events providing insufficient cues. Last but not least, when applying to high-speed scenarios, the synchronization of sensors could be important issue as we need the frame and events to capture the scene with similar timing, and specifically designed electronic system [72] could be the solution. We aim to work on enhancing the pipeline and generalizing method further.
10

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Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 62136001, 62088102, 62402014), Beijing Natural Science Foundation (Grant No. L233024), and Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park (Grant No. Z241 100003524012). Peiqi Duan was also supported by China National Postdoctoral Program for Innovative Talents (Grant No. BX20230010) and China Postdoctoral Science Foundation (Grant No. 2023M740076). The authors thank pttttp:://oopnbayyee com for providing computing resources.
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Appendix
Our Appendix is organized as follows: First of all, we give additional mathematical derivation in
Sec. A Dataset construction details are provided in Sec. Computational efficiency comparison is
discussed in C Finally, we show more qualitative and quantitative results on our four datasets Sec.
p
A
Additional mathematical derivation
Observation 2: The sign of the right side of Eq. (5) in the main paper will change before and after focusing.
Suppose the pixel (zo. Yo) is in the foreground, and l v(x ra)2 (y yo) be the distance of (In, yo) to (x, y)- When the radius of CoC, 0 < r, the pixel (xc, y) will not be affected by bokeh effects, so Py (z, y, O.
When 2 r. according to previous studies [54 17 PSF function of (mo: can be approximated as Gaussian function, which can be formulated as:
T
(- (x ( xo)² + (v- y0)² )
Pz v, = 2TO² exp
0,
(13)
202
where T is the amplitude, and is the spread parameter proportional to 6, i.e,, = k0.
Combining Eq. (1) in the main paper and the above analysis, have: When F approaches from a small distance to the foreground depth D,. 0 will gradually transfer from a large value to and (2 y, () will decrease from a positive value O, i.e., A(P y, ()) 0. Similarly, when F moves from D to a larger value, 0 will increase, and thus (E, > 0.
Observation 3: If the antensity-changiin pixel is the texture of single object instead of an edge separating two objects, the triggered events around it will also experience a polarity reversal before and after focusing.
Suppose the texture splits the object into two parts with individual uniform intensity and Iz. respectively. Then we derive the intensity of a pixel (I1,81) in Part can be formulated as:
I( V1. t) = 1 PP((t.
(14)
where P(t) is the PSF of the object. Then the log-scale intensity change can be derived as:
I I2P(t) = log(1 + 12 I 12P(1 At)
P(t) P(t - At)
A log(([(1, = log
(15)
I I,P(1- - At)
Therefore,
sign log(I(<); U1, t))) sign(AP(t)). Similar to the above derivation, for a pixel (x2 1/2) in Part 2, we have
(16)
sign( A log Log(I(22. V2, t))) sign(AP(tt).
(17)
So the intensity changes in both parts will undergo polarity reversal before and after focusing. If these changes trigger events, a polarity reversal will also occur.
B More dataset construction details
Blender-Syn. We first select geometric objects, and then rescale and rotate them randomly in the scene. Then, the objects are put at different locations within depth range from Im to 30m. After that, we wrap them with textures sampled from Poly Haven 44)] Finally, we sweep the focus depth of our camera from 0.1m to 30m to generate focus sweeping video of 500 frames, with an aperture f/1.4 and focal length 100mm. For each frame, we add slight motion and rotation each object in the scene to simulate the shaking in real world. Besides, the AIF image is rendered using small aperture (f/1000). The scenes are rendered using Blender Cycles engine with reflection turned on, but with shadows off. Each image has resolution 720 1280. Although the scene seems unrealistic
23

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alab.
CIsco
Figure 6: Example patterns we use for capturing EDFV-Real dataset.
Table 4: V2E parameters we adjust for simulating realistic events.
Parameter
Description
Our value
Default value
shot-noise-rate-hz
Temporal noise rate of events in darkest parts of scene. Leak event rate per pixel in Hz.
0.1
0.001
leak-rate-hz
0.5
0.01
to some extent, our main purpose is to guide the models to learn focus/defocus cues instead of spatial and shape cues.
In order to improve the imput information integrity, we evenly split the rendered frame focal stacks into 5 consecutive segments, and choose one frame from each segment the input to frame- -asee DFF methods. Besides, for our method, we use V2E [24] to simulate events from the focal stack video. In order to add noises with reality, manually adjust some parameters of V2E shown in Table The AIF image is used to be fed into single-image-base -basd methods.
Sintel-Dr. Bokeh. Since the GT of test set for Sintel [6] dataset has not been released, we randomly sample its training set our dataset. Each image has resolution 436 1024. We use their AIF images and GT depths as input to the bokeh rendering method Dr. Bokeh (58). The blur strength parameter of Dr. Bokeh is set to 30 for more salient defocus cues. The biggest lens kernel size is 71, and the gamma parameter is 2.2. The focus depth is swept from 3. to 30m. After generating the focus sweeping video, the post process for generating input to methods is the same as Blender-Syn.
ADLFD-Semi-Rees We follow AiFDepthNet 62 to generate AIF images and focal stacks from the light field data of 4D Light Field Dataset (21). Then concatenate the focal stacks into video with 50 FPS, and play the video on a screen to simulate the focus sweeping process. A Prophesee EVK4 event camera is employed record the process and generate real events for this semi-real dataset, which is for zero-shot testing only. It should be noted that the original data resolution is 512 x 512, while our captured events have resolution 742 720. When evaluating our method this dataset, we first interpolate the input images to the event resolution, and finally resize the output depth the original resolution for computing metrics.
EDFV-Real. order to evaluate the performance of single-image-based, DFF and our methods in real-world scenarios, we further capture real dataset with our hybrid camera system. Our hybrid camera system consists of a machine vision camera (HIK VISION MV-CA050-12U camera (Prophesee EVK4). The two cameras co-aligned with beam splitter. Both of them use 16 mm lens (HIKVISION MVLLMMFI62MM- -MP). Since this lens did not exhibit noticeable FoV breathing during observations, did not account for this effect in data acquisition. Besides, we use checkboards to calculate the homography matrix to ensure alignments between these two cameras before capturing.
and an event
During capture, we manually set scenes to "cheat" single-image- methods. One way is to print warped textures on thin paper to confuse them with singular and gradient depths. Some patterns we use are shown in Fig. G These patterns successfully mislead single-image-basee methods (refer to qualitative results), while they can be distinguished by method.
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C Computational efficiency
We compare the number of parameters, Multiply-Aceuminn Accumulate Operations (MACs) and inference time with other methods in Table The MACs and inference times are calculated with unified resolution 224 224, and all DFF methods use 5 frames as input. The runtimes are reported from a signle NVIDIA RTX 4090 GPU. Note the statistics of HybridDepth and our method in the table has excluded IFM. From the comparison, we can observe that our method introduces only 5.96 + 332.68 a 1.79% additional parameters and 15.44 + 109.75 78 R 14.06% additional MACs compared with the original DA V2. Besides, the runtime of our method is comparable to most other methods.
Additionally, we add the statistics of the ablation study of "Ours-FS FS" in Sec. 5.5 where we replace the input event with 32-frame focal stacks. It can be observed that this could bring 20.41 + 5.96 3.42 times parameter numbers and 134.91 + 15.44 R 8.74 times MACs to the whole pipeline, which is not desirable for essurcee-lmitee devices. Instead, our event-based method can achieve balance between accuracy and efficiency. and is more practical to deploy on -ouurce-limited devices.
Table 5: Computational efficiency comparison with other methods.
Method
Type
Params(M)
MACs(G)
Time(ms)
DefocusNet
DFF
3.72
29.57
13.69
DFF-FV
DFF
18.40
20.54
27.35
DFF-DFV
DFF
18.40
20.54
28.40
DDFS
DFF
25.44
72.39
3,73
HephidDepth
DFF
34.97
24.11
32.96
DA V2
Mono
332.68
109.78
23.59
Metric3D V2
Mono
302.92
72.03
55.14
Mono
47.75
14.16
42.37
Ours-FS
DFF
20.41
134.91
28,40
Ours
DFF
5.96
15.44
17.40
D More results
More qualitative results with reconstructed point clouds of in-domain experiments on Blender-Syn dataset in Fig. and Fig. & and Sintel-Dr. Bokeh dataset in Fig. 9 Qualitative results of zero-shot experiments on Blender-Syn are shown in Fig. I0 and Sintel-Dr. Bokeh are shown in Fig. From the comparison, can observe the more accurate prediction results of method.
Zero-shot experiment quantitative results on 4DLFD-Semi LLFFF----iiReaa Real dataset are shown in Table and qualitative results in Fig. [2] Note that although our method may predict less accurate results in closer regions compared with Metric3D V2, possibly due to fewer textures, we achieve more accurate predictions in farther regions.
Figure 13 shows more qualitative results on EDFV-Real dataset. In the first sene, our method successfully ly distinguishes the pattern printed on the paper (object @), while DA V2 and Metric3D V2 predict gradient depth. In the second scene, we place three ducks different sizes (objects @ @ and @) at unified depth from the camera view. DA V2 predicts different depths for them due to scale ambiguities, while our method yields a reasonable result.
25

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Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybridDepth
Events
DA V2
Metric3D V2
DAC
Ours
GT
Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybridDepth
Events
DA
Metric3D
DAC
Ours
GT
Image
DefocusNet
DFF-VV
DFF-DFV
DDFS
HybridDppth
Events
DA
Metric3D
DAC
Ours
GT
Figure 7: More qualitative results of in-domain experiments on Blender-Syn dataset with the input image and events (Part I). Each consecutive 4 rows corresponds to one test sample. Each odd row shows the input RGB image/events and predicted depth maps. Each even row shows reconstructed point clouds. We compare with DefocusNet [40]. DFF-FV (64, DFF-DFV (64. DDFS [3]
HybridDepth 14, DA V2 67 Metric3D V2 23 and DAC [6] Please zoom in for more details.
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Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybridDepth
Events
DA
Metric3D V2
DAC
Ours
GT
Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybridDepth
Events
DA V2
Metric3D V2
DAC
Ours
GT
Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybridDepth
Events
DA V2
Metric3D V2
DAC
Ours
GT
Figure 8: More qualitative results of in-domain experiments on Blender-Syn dataset with the input image and events (Part II).
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Table 6: Quantitative comparisons of zero-shot metric depth estimation on 4DLFD--emi-Real
Method
Type
RMSF()
RMSE log(L)
log10(4)
61(1)
d2(()
da(1)
DFF-FV
DFF
1.979
0.198
0.070
0.680
0.888
0.949
DFF-DFV
DFF
1.943
0.186
0.064
0.711
0.902
0.953
DDFS
DFF
1.680
0.167
0.060
0.772
0.918
0.956
HybridDepth
1.573
0.135
0.048
0.818
0.940
0.957
DA V2
Mono
2.997
0.227
0.088
0.561
0.897
0.958
Metric3D V2
Mono
2.972
0.221
0.085
0.594
0.892
0.953
DAC
Mono
2.915
0,229
0.087
0.590
0.889
0.953
Ours
DFF
1.549
0.128
0.047
0.832
0.957
0.958
Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybridDepth
Events
DA
Metric3D V/2
DAC
Ours
GT
Image
DefocusNet
DFF-FV
DFF-DFV
DDFS
HybriDDpthh
Events
DA
Metric3D V/2
DAC
Ours
Figure 9: More qualitative results of in-domain experiments on Sintel-Dr. Bokeh dataset with the input image and events.
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---

Image/Events
DFF-FV
DFF-DFV
Metric3D V2
Ours
GT
Figure 10: Qualitative results of zero-shot experiments Blender-Syn dataset with the input image and events. We compare with DFF-FV 664,, DFF-DFV [64), and Metric3D V2 [23
Image/Events
DFF-FV
DFF-DFV
Metric3D V2
Ours
GT
Figure 11: Qualitative results of zero-shot experiments on Sintel-Dr. Bokeh dataset with the input image and events.
Image
Events
AIFDepthNet
DFF-FV
DFF-DFV
HybridDepth
Metric3D
Ours
Figure 12:
Qualitative results of zero-shot experiments on ADLFD-Semi-- Meni-Real dataset with the input image and events. We compare with DEF methods AiFDepthNet [62], DFF-FV, DFF-DFV [64], HybridDepth [14), and single-imagc-based Metric3D V2 23
29

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Image
AiFDepthNet
DFF-FV
DFF-DFV
Camera view
Scene
Events
DA V2
Metric3D V2
Ours
Image
AiFDepthNet
DFF-FV
DFF-DFV
Camera
view
Scene
Events
DA V2
Metric3D V2
Ours
Figure 13: Qualitative results of zero-shot experiments on EDFV-Real dataset. Each pair of consecu- tive rows corresponds one test scene. The left side of the dashed line presents the scene overview, with the cones representing camera views, while the right side displays the input along with the outputs various methods. Each number the same scene (i.e., @, @ and @) corresponds to same object. Inverse depths are visualized here. Details are in the text.
30


Reviews:
[{"review_id": "BFqWXNYUmm", "reviewer": "Reviewer_CTBx", "summary": "This paper proposes a depth estimation framework that combines event camera data generated during focus sweeping with image foundation models via a prompting strategy. It introduces the Event-based Differential Focus Volume (EDFV) for sparse metric depth estimation and designs an Event-based Depth Prompting Network (EDPN) to refine dense depth predictions. The authors also construct synthetic and real datasets to evaluate their method.\n\n1. EDFV (Event-based Differential Focus Volumes) for extracting sparse metric depth from events.\n2. EDPN (Event-based Depth Prompting Network) that fuses sparse event depth and dense IFM predictions to produce final accurate depth.\n3. Construction of two synthetic datasets and two real/semi-real datasets with events, AIF images, and GT depth for training and evaluation.\n4. Superior in-domain and zero-shot performance compared to state-of-the-art DFF and single-image methods across multiple datasets.", "questions": "1. While the proposed EDFV performs well overall, it's unclear how it handles textureless regions or areas with low event density. Could the authors show or describe failure cases where EDFV performs poorly? Do you apply any fallback mechanism or fusion weighting in such cases?\n\n2. In your real-world dataset (EDFV-Real), how did you ensure temporal synchronization between the event camera and RGB sensor, especially during fast focus sweeping? Were there any delays or misalignment corrections applied? Accurate depth estimation heavily depends on synchronization.", "limitations": "1. The method relies on event triggers from intensity changes, which may be sparse or absent in textureless or poorly lit regions.\n\n2. The proposed system requires precise synchronization between RGB and event cameras and a controllable focus mechanism. These requirements limit real-world usability, especially for mobile or embedded systems.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nQuality :\nThe proposed method is well-motivated and carefully designed. The derivation of the polarity reversal property in Section 3.2 shows a solid understanding of event camera properties. Extensive experiments are conducted on four datasets (two synthetic, two real/semi-real), with both in-domain and zero-shot evaluations.\n\nClarity:\nThe paper is well-organized, with clearly separated contributions and pipeline stages.\n\nSignificance:\nThe integration of event-based sparse depth with foundation model prompting addresses the critical problem of scale ambiguity in monocular depth estimation. The event-guided prompting idea may influence future depth estimation systems or even broader tasks involving hybrid sensing and prompting.\n\nOriginality:\nThe proposal of EDFV is novel in the context of depth estimation, leveraging event-triggered defocus polarity—a concept not previously exploited in this form. The method diverges meaningfully from both traditional DFF and event-based monocular methods. The use of deformable convolutions in cross-attention for misalignment correction is an innovative adaptation to the challenges of hybrid sensor data.\n\nWeaknesses:\n\nClarity:\nSome important details are left to the Supplementary Material (e.g., more mathematical derivation of event polarity behavior, ablation studies on hyperparameters). The limitations section is quite short. A more systematic reflection on assumptions (e.g., sensitivity to event noise, synchronization of sensors) would improve transparency.\n\nSignificance:\nWhile promising, the real-world applicability is still partially constrained by the hardware complexity of event-RGB hybrid systems and the assumption of focus sweeping capability. These aspects are not discussed in depth.\n\nOriginality:\nThe idea of prompting IFMs with sparse depth is increasingly common (e.g., in LiDAR prompting literature). Although the paper adapts this idea well to event data, the prompting framework (EDPN) may not be as novel as the EDFV component.", "was_revised": true, "final_justification": "The authors have addressed my concerns raised during the review process.", "rebuttal": "We sincerely thank you for your insightful comments and constructive suggestions! We are encouraged that you find our method well-motivated and carefully designed, our paper well-organized, and our concept novel. Below, we provide detailed responses to your proposed Weaknesses (**W**), Questions (**Q**), and Limitations (**L**).\n> **W1.1**: *Some important details are left to the Supplementary Material.*\n\nThank you for your suggestions! Due to the page limit of the submitted version for peer review, we have to put some details in the Supplementary Material. However, we understand the importance of these components in demonstrating the validity and interpretability of our proposed approach, and will include the important details in the final version of our main paper if accepted as follows:\n\n- We will put **Additional Observation 1&2** (Supplementary Material Sec. A) and their **brief derivations** in the main paper (Sec. 3.2), while retaining the extended math in the Supplement. \n\n- Since the construction of the datasets is one of our core contributions, we will migrate **more dataset construction details** (Supplementary Material Sec. B) to the main paper (Sec. 5.1), including the camera parameters and scene setup details.\n\n- We will move the **ablation studies** (Supplementary Material Sec. D and Table 5) to the main paper as **Sec. 5.5**, following Sec.5.4.\n\nWe will condense our texts to make sure the final version of our main paper within page limit, concise, and more inclusive.\n> **W1.2**: *The limitations section is quite short. A more systematic reflection on assumptions (e.g., sensitivity to event noise, synchronization of sensors) would improve transparency.*\n\nWe appreciate this suggestion. While our method has shown robustness in various scenarios,  we acknowledge its performance may degrade under extreme conditions. Specifically:\n- In **low light scenarios**, high sensor noise can reduce the quality of EDFV and predicted sparse depth, thus causing the performance of our method to degrade.\n- When applied to **high-speed scenarios**, the synchronization of sensors could be an important issue as we need the frame and events to capture the scene with similar timing. But there are some approaches such as the chip synchronization in DAVIS346 [1] and external clock triggering mechanism [2] to limit the time error within 10ms. Besides, a specifically designed electronic system [6] could reach the time precision of **0.5ms**. These hardware solutions could enable our system to achieve high-precision synchronization. \n\nWe will add these discussions in the final version of our paper, and try to improve the robustness of our method in these challenging scenarios in our future work.\n> **W2**: *While promising, the real-world applicability is still partially constrained by the hardware complexity of event-RGB hybrid systems and the assumption of focus sweeping capability. These aspects are not discussed in depth.*\n\nThank you for highlighting these practical considerations. \n- For the hardware complexity, our current setup is just a **camera prototype**, and the hybrid system could be further integrated by engineering approches to reduce the complexity. In addition, as far as we know, some industrial camera modules could reduce the focus ring rotation time to about **10ms** such as the liquid lens, which could further improve the real-time efficiency of our system.\n- For the assumption of focus sweeping capability, as indicated in the mathematical derivations in Sec. 3.2 and Supplementary Material Sec. A, the effectiveness of events mainly locates at edges and textures. Therefore, for textureless regions or areas with low event density, our estimation relies more heavily on the initial dense depth and sparse depth around textured regions. This may cause our method to perform suboptimally in **highly smooth scenarios**. Notably, such scenarios also remain **open challenges** for conventional DFF approaches [3], as they depend on textures to estimate the in-focus degree. \n\nWe will add these discussions in the final version of our paper, and work on improving the robustness of our method in these scenarios in future work.\n> **W3**: *The idea of prompting IFMs with sparse depth is increasingly common (e.g., in LiDAR prompting literature). The prompting framework (EDPN) may not be as novel as the EDFV component.*\n\nThank you for this thoughtful comment. We agree that the general idea of prompting image foundation models (IFMs) with sparse depth has gained increasing attention, such as in the context of LiDAR [4,5]. However, although **the idea is common, the strategy may differ**.  Therefore, we respectfully argue that our approach differs from previous works in motivation and implementation. It should be noted that the novelty of our work should be viewed from the **holistic** perspective of the proposed framework, compromising the EDFV construction, sparse depth & initial dense depth estimation, and prompting network (EDPN) (please refer to the proposed second contribution in our paper L63-65). Each component in the pipeline is essential to the overall method (as discussed in **Ablation studies** in Supplementary Material Sec. D). While a single component may seem straightforward, our combined pipeline introduces a cohesive and tailored framework that enables effective prompting in a challenging hybrid modality. \n\n As discussed in the **Introduction (L48-50)**, the information given by event-based sensors is different from other sensors such as LiDAR, as it mainly triggers at texture and edge regions. This poses unique challenges and opportunities for our design. \n\nIn our pipeline, we first utilize proposed EDFV to process events into depth map. After that, we design a SAME strategy to extract effective information from events (**L179-181** of our paper). Besides, considering the modality discrepancies between events and image, we employ different layers to capture their features respectively (**L199-201** of our paper). We also introduce deformable convolutions to handle the sparsity and possible misalignments between two modalities (**L207-209** of our paper). **We will revise the paper to clarify this contribution and better differentiate it from related works.**\n\n> **Q1&L1**: *The method relies on event triggers from intensity changes, which may be sparse or absent in textureless or poorly lit regions. Could the authors show or describe failure cases where EDFV performs poorly? Do you apply any fallback mechanism or fusion weighting in such cases?*\n\n Please refer to our rebuttal in **W1.2** and **W2** for some challenging scenarios where our method may perform suboptimally. As we have considered the feature of sparse events in our design of prompting network, we did not incorporate a fallback mechanism in our method. The deformable convolutions and cross attention mechanism in our pipeline could help to handle the textureless regions. However, we agree that an additional fallback mechanism or fusion weighting may further improve the performance in extremely challenging conditions. These improvements are beyond our current scope, and will be considered as our future work.\n> **Q2&L2**: *The proposed system requires precise synchronization between RGB and event cameras and a controllable focus mechanism. These requirements limit real-world usability. How to ensure temporal synchronization between the event camera and RGB sensor, especially during fast focus sweeping?*\n\nIf applied to static or slow motion scenarios, the synchronization between the event camera and RGB sensor may be less crucial, as they record nearly the same scene over short durations. But if applied to high-speed scenarios, tighter synchronization could be required. As mentioned in **W1.2**, hardware-based solutions such as externally triggered clocking mechanisms [2] are for maintaining reliable timing within temporal tolerances.\n\n[1] Zhu et al. The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception. RAL 2018.\n\n[2] Duan et al. EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset. TPAMI 2025.\n\n[3] Yang et al. Deep Depth from Focus with Differential Focus Volume. CVPR 2022.\n\n[4] Park et al. Depth Prompting for Sensor-Agnostic Depth Estimation. CVPR 2024.\n\n[5] Lin et al. Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation. CVPR 2025.\n\n[6] Zou et al. Learning to Reconstruct High Speed and High Dynamic Range Videos from Events. CVPR 2021."}, {"review_id": "eUKBx2rSLv", "reviewer": "Reviewer_hHyQ", "summary": "This paper proposes to fuse events with depth foundation models for metric depth estimation. The main contributions include 1) a EDFV representation that encodes focus sweeping cues from events. 2) a framework that combines events and depth foundation model for metric depth estimation. 3) novel datasets for proposed task. Experimental results on Blender-Syn and Sintel-Dr. Bokeh datasets demonstrate the superiority of proposed method.", "questions": "- The necessity of using events for focus/defocus cue encoding is not sufficiently justified. The data collection process still relies on focal length changing, and the authors note that event cameras are used to record focal length changes more frequently. However, since focal length changes are relatively slow, it is unclear whether such frequent recording is necessary, as standard cameras can capture more than 30 frames per second during focal length changes. This raises questions about whether a focal stack constructed from standard cameras is sufficient for accurate depth estimation. Additionally, events triggered by factors other than focal length changes may introduce noise, potentially degrading depth estimation performance. The authors should provide further analysis to demonstrate the superiority of using event cameras over traditional depth-from-focus methods.  \n- Based on the above question, if we replace the sparse depth generation step with normal depth-from-focus approaches, and keep the subsequent steps (MCPM part) unchanged, how would the performance be affected?\n-  It is suggested to indicate the type of compared method (such as DFF and Mono) in Table 1 and 2 for better clarity.", "limitations": "The limitation is discussed in the main text.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength\n- The exploration of encoding focus/defocus cues from events addresses an underexplored task in the field.\n- The proposed method demonstrates superior performance compared to existing approaches.\n- The introduction of new datasets is a valuable contribution to the community.\n\nWeakness\n- The frequent use of abbreviations throughout the paper may confuse readers, reducing clarity.\n- The paper’s structure needs improvement. Notably, the Ablation Study section is recommended to put in the main text.\n- The experimental evaluation lacks comparisons with state-of-the-art approaches from the past two years, which weakens the claim of superior performance.\n- Although the 4DLFD-Semi-Real and EDFV-Real datasets are introduced, no quantitative experiments are conducted on these datasets.\n- Some typo: L197, \"we two\".", "was_revised": true, "final_justification": "Please see the comment.", "rebuttal": "We sincerely thank you for your insightful comments and constructive suggestions! We are encouraged that you find our idea novel, our performance superior, and our dataset valuable to the community. Below, we will address your proposed Weaknesses (**W**) and Questions (**Q**) in detail.\n> **W1**: *The frequent use of abbreviations throughout the paper may confuse readers.*\n\nThank you for your feedback. We will revise the entire manuscript to minimize the use of unnecessary abbreviations, and ensure they are clearly defined at their first appearance in the main text to improve readability.\n> **W2**: *The paper’s structure needs improvement. The Ablation Study section is recommended to put in the main text.*\n\nThank you for your suggestions! Due to the page limit of the submitted version for peer review, we have to put some details in the Supplementary Material. However, we understand the importance of these components in demonstrating the validity and interpretability of our proposed approach, and will include the important details in the final version of our main paper if accepted as follows:\n\n- We will put **Additional Observation 1&2** (Supplementary Material Sec. A) and their **brief derivations** in the main paper (Sec. 3.2), while retaining the extended math in the Supplement. \n\n- Since the construction of the datasets is one of our core contributions, we will migrate **more dataset construction details** (Supplementary Material Sec. B) to the main paper (Sec. 5.1), including the camera parameters and scene setup details.\n\n- We will move the **ablation studies** (Supplementary Material Sec. D and Table 5) to the main paper as **Sec. 5.5**, following Sec.5.4.\n\nWe will condense our texts to make sure the final version of our main paper within page limit, concise, and more inclusive.\n\n> **W3**: *The experimental evaluation lacks comparisons with state-of-the-art approaches from the past two years.*\n\nWe respectfully clarify that **Depth Anything V2** [1] and **Metric3D V2** [2] are both state-of-the-art depth estimation methods published in the year 2024. In addition, we have now incorporated a new method, namely **Depth Any Camera (DAC)** [3], published in 2025 to further extend the scope of compared methods. Here we finetune DAC using its pretrained outdoor ResNet101 checkpoint. Please refer to the following tables for our comparison, where we denote DFF methods as \"DFF\" and monocluar methods as \"Mono\". We will update the Table 1 and 2 in our final version accordingly. Note that DAC may not perform well because its main focus is wide-angle cameras.\n# Table R1: Quantitative comparisons of in-domain metric depth estimation. \n## Blender-Syn\nMethod|Type|RMSE(↓)|AbsRel(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.243|0.372|0.107|0.734|0.818|0.861\nDFF-FV|DFF|0.184|0.223|0.062|0.862|0.907|0.926\nDFF-DFV|DFF|0.186|0.250|0.062|$\\underline{0.871}$|0.906|0.923\nDDFS|DFF|0.244|0.387|0.109|0.723|0.804|0.849\nDA V2|Mono|$\\textbf{0.063}$|$\\underline{0.089}$|$\\underline{0.035}$|0.865|$\\underline{0.956}$|$\\textbf{0.989}$\nMetric3D V2|Mono|0.095|0.162|0.062|0.826|0.934|0.973\nDAC|Mono|0.176|0.238|0.115|0.654|0.868|0.947\nOurs|DFF|$\\underline{0.068}$|$\\textbf{0.077}$|$\\textbf{0.028}$|$\\textbf{0.919}$|$\\textbf{0.972}$|$\\underline{0.987}$\n## Sintel-Dr. Bokeh\nMethod|Type|RMSE(↓)|AbsRel(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.209|0.797|0.192|0.412|0.644|0.728\nDFF-FV|DFF|0.160|0.661|$\\underline{0.109}$|$\\underline{0.766}$|$\\underline{0.863}$|0.898\nDFF-DFV|DFF|$\\underline{0.134}$|0.569|$\\underline{0.109}$|0.738|0.861|$\\underline{0.907}$\nDDFS|DFF|0.282|1.072|0.282|0.441|0.578|0.648\nDA V2|Mono|0.297|0.482|0.361|0.330|0.419|0.472\nMetric3D V2|Mono|0.170|$\\underline{0.479}$|0.174|0.452|0.561|0.754\nDAC|Mono|0.273|0.951|0.289|0.268|0.409|0.573\nOurs|DFF|$\\textbf{0.095}$|$\\textbf{0.141}$|$\\textbf{0.072}$|$\\textbf{0.806}$|$\\textbf{0.901}$|$\\textbf{0.945}$\n# Table R2: Quantitative comparisons of zero-shot metric depth estimation. \n## Blender-Syn\nMethod|Type|RMSE(↓)|RMSE log(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.425|0.783|0.292|0.135|0.391|0.608\nDFF-FV|DFF|0.325|0.661|$\\underline{0.162}$|$\\underline{0.669}$|$\\underline{0.732}$|0.775\nDFF-DFV|DFF|0.369|0.710|0.196|0.651|0.681|0.707\nDDFS|DFF|0.495|1.120|0.377|0.287|0.361|0.448\nDA V2|Mono|0.725|1.913|0.783|0.018|0.057|0.108\nMetric3D V2|Mono|$\\underline{0.294}$|$\\underline{0.535}$|0.207|0.343|0.625|$\\underline{0.777}$\nDAC|Mono|0.652|1.488|0.594|0.075|0.142|0.198\nOurs|DFF|$\\textbf{0.148}$|$\\textbf{0.282}$|$\\textbf{0.081}$|$\\textbf{0.697}$|$\\textbf{0.878}$|$\\textbf{0.944}$\n## Sintel-Dr. Bokeh\nMethod|Type|RMSE(↓)|RMSE log(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.518|1.504|0.585|0.123|0.204|0.275\nDFF-FV|DFF|$\\underline{0.267}$|$\\underline{0.982}$|$\\underline{0.343}$|0.177|0.364|$\\underline{0.518}$\nDFF-DFV|DFF|0.270|1.038|0.366|0.192|0.332|0.495\nDDFS|DFF|0.706|1.852|0.726|0.203|0.231|0.251\nDA V2|Mono|0.337|1.048|0.396|0.242|$\\underline{0.391}$|0.461\nMetric3D V2|Mono|0.322|1.174|0.469|$\\underline{0.262}$|0.369|0.466\nDAC|Mono|0.515|1.474|0.581|0.144|0.228|0.285\nOurs|DFF|$\\textbf{0.233}$|$\\textbf{0.685}$|$\\textbf{0.253}$|$\\textbf{0.333}$|$\\textbf{0.466}$|$\\textbf{0.560}$\n> **W4**: *No quantitative experiments are conducted on 4DLFD-Semi-Real and EDFV-Real datasets.*\n\nWe now provide the **quantitative results** on the 4DLFD-Semi-Real dataset in the following table. Here we convert all disparity predictions into depth values with unified curves for fair comparisons. It can be found that our method outperforms others in all metrics.\n# Table R3: Quantitative comparisons of zero-shot metric depth estimation on 4DLFD-Semi-Real. \nMethod|Type|RMSE(↓)|RMSE log(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDFF-FV|DFF|1.979|0.198|0.070|0.680|0.888|0.949\nDFF-DFV|DFF|1.943|0.186|0.064|0.711|0.902|0.953\nDDFS|DFF|$\\underline{1.680}$|$\\underline{0.167}$|$\\underline{0.060}$|$\\underline{0.772}$|$\\underline{0.918}$|$\\underline{0.956}$\nMetric3D V2|Mono|2.972|0.221|0.085|0.594|0.892|0.953\nDAC|Mono|2.915|0.229|0.087|0.590|0.889|0.953\nOurs|DFF|$\\textbf{1.549}$|$\\textbf{0.128}$|$\\textbf{0.047}$|$\\textbf{0.832}$|$\\textbf{0.957}$|$\\textbf{0.958}$\n\nFor EDFV-Real, due to the absence of a depth sensor in our current system setup, we were unable to collect ground-truth depth maps for quantitative evaluation. Please refer to Fig. 5 of our paper and Fig. 12 of Supplementary Material for qualitative comparisons. In the future work, we aim to enhance our hardware system by integrating a depth sensor for accurate ground-truth depth capture, enabling quantitative analysis.\n> **W5**: *Some typo: L197, \"we two\".*\n\nThank you for spotting this. Here we have left an \"employ\", which should be \"we employ two U-Net-based networks\". We will thoroughly revise the manuscript and eliminate all such typographical errors in our final version.\n> **Q1&Q2**: *The authors should provide further analysis to demonstrate the superiority of using event cameras over traditional depth-from-focus methods. Based on the above question, if we replace the sparse depth generation step with normal depth-from-focus approaches, and keep the subsequent steps (MCPM part) unchanged, how would the performance be affected?*\n\nWe appreciate this insightful question. We respectfully demonstrate the necessity of introducing events in the following three aspects:\n\n1. Our current setup is just a **camera prototype**, and the rotation speed is limited by mechanical or manual speed. However, as far as we know, some industrial camera modules could reduce the focus ring rotation time to about **10ms** such as the liquid lens, which would be hard for standard cameras to capture focal stacks. However, event cameras can easily handle the scenario with a **microsecond-level** temporal resolution.\n\n2. As discussed in **L171-174** of our paper, the performance of traditional DFF methods could be influenced by the **discrete** frame number in focal stacks. However, with events, we can capture the focal stack with a microsecond-level temporal resolution, which can provide **nearly continuous** transitions between in-focus and out-of-focus regions, and help to improve the accuracy of depth estimation. In addition, although events may be triggered less in **smooth or textureless** regions, traditional DFF methods also struggle in such areas [4] as they depend on textures to estimate the in-focus degree. Therefore, even if replacing the sparse depth generation step with normal DFF approaches, the performance **may not increase**.\n\n3. As events only record intensity changes, the total raw data size with resolution $720\\times 1280$ during one capture is averagely **4-5MB**. However, a single image in the focal stack with the same resolution could take up to 2.7M, with 30 images up to **80MB**. This means that the storage space required for event-based depth estimation is much smaller than that of traditional DFF methods, which make it more practical to deploy on resource-limited devices.\n> **Q3**: *It is suggested to indicate the type of compared method (such as DFF and Mono) in Table 1 and 2 for better clarity.*\n\nThank you for your suggestions. We have provided the updated version of Table 1 and 2 in the tables above, and will also include them in our final version.\n\n[1] Yang et al. Depth Anything V2. NeurIPS 2024.\n\n[2] Hu et al. Metric3d V2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation. TPAMI 2024.\n\n[3] Guo et al. Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera. CVPR 2025.\n\n[4] Yang et al. Deep Depth from Focus with Differential Focus Volume. CVPR 2022."}, {"review_id": "gN9R2ZvMb4", "reviewer": "Reviewer_kC23", "summary": "This paper introduces a novel approach for dense metric depth estimation by fusing events with image foundation models (IFMs) via a prompting strategy.\nThe authors propose Event-based Differential Focus Volumes (EDFVs) using events triggered by focus sweeping, transforming them into sparse depth maps to prompt dense estimation via the Event-based Depth Prompting Network (EDPN) and construct synthetic and real datasets for training and evaluation. Experiments show the method outperforms state-of-the-art approaches, achieving up to 29.1% improvement in RMSE.", "questions": "Please see 2) and 3) in weakness.", "limitations": "Please see the weakness.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Innovative Modality Fusion: Proposes a novel framework fusing event cameras with image foundation models (IFMs), addressing scale ambiguity in single-image methods and low sampling rates in traditional depth-from-focus approaches.\n2. Comprehensive Dataset Construction: Creates synthetic (Blender-Syn, Sintel-Dr. Bokeh) and real-world datasets (4DLFD-Semi-Real, EDFV-Real) with ground truth, enabling rigorous evaluation of frame-based and event-based methods.\n3. Superior Performance Validation: Achieves up to 29.1% RMSE improvement over state-of-the-art methods, excelling in in-domain and zero-shot scenarios for textureless regions, scale ambiguity, and occlusion.\n\n\nWeakness:\n1. The organization of the paper could be improved. Ablation studies, as an essential part of the experimental analysis, should be presented in the main text rather than being relegated to the supplementary material. Moreover, since the construction of the two datasets is highlighted as one of the core contributions in the Introduction, a more detailed description of the dataset creation process should also be included in the main body of the paper, rather than placing the majority of this information in the supplementary material.\n2. The comparative analysis lacks baseline methods from 2025, which may limit the completeness and timeliness of the evaluation.\n3. The experimental section in the main text appears limited in scope. There are no quantitative results reported for the 4DLFD-Semi-Real setting or the proposed dataset. Additionally, the set of comparison models does not include the event-based DFF methods mentioned in the Related Work section.", "was_revised": true, "final_justification": "I noticed that more than one reviewer pointed out issues regarding the allocation of space in the paper, such as the absence of ablation studies in the main text and limitations in the comparative experiments. After considering the authors' rebuttal and the feedback from other reviewers, I still think the paper falls slightly below the NeurIPS acceptance threshold. I will maintain my original score. I encourage the authors to revise the paper’s structure and experimental presentation in future versions.", "rebuttal": "We sincerely thank you for your valuable feedback and constructive suggestions! We are encouraged that you find our framework novel, dataset construction comprehensive and performance superior. Below, we will address your proposed Weaknesses (**W**) in detail.\n> **W1**: *The organization of the paper could be improved. Ablation studies and  a more detailed description of the dataset creation process should be placed in the main paper.*\n\nThank you for your suggestions! Due to the page limit of the submitted version for peer review, we have to put some details in the Supplementary Material. However, we understand the importance of these components in demonstrating the validity and interpretability of our proposed approach, and will include the important details in the final version of our main paper if accepted as follows:\n\n- We will put **Additional Observation 1&2** (Supplementary Material Sec. A) and their **brief derivations** in the main paper (Sec. 3.2), while retaining the extended math in the Supplement. \n\n- Since the construction of the datasets is one of our core contributions, we will migrate **more dataset construction details** (Supplementary Material Sec. B) to the main paper (Sec. 5.1), including the camera parameters and scene setup details.\n\n- We will move the **ablation studies** (Supplementary Material Sec. D and Table 5) to the main paper as **Sec. 5.5**, following Sec.5.4.\n\nWe will condense our texts to make sure the final version of our main paper within page limit, concise, and more inclusive.\n> **W2**: *The comparative analysis lacks baseline methods from 2025.*\n\nThank you for highlighting this. We have now incorporated a new method, namely **Depth Any Camera (DAC)** [1], published in 2025 to further extend the scope of compared methods. Here we finetune DAC using its pretrained outdoor ResNet101 checkpoint. Please refer to the following tables for our comparison, where we denote DFF methods as \"DFF\" and monocluar methods as \"Mono\". We will update the Table 1 and 2 in our final version accordingly. Note that DAC may not perform well because its main focus is wide-angle cameras.\n# Table R1: Quantitative comparisons of in-domain metric depth estimation. \n## Blender-Syn\nMethod|Type|RMSE(↓)|AbsRel(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.243|0.372|0.107|0.734|0.818|0.861\nDFF-FV|DFF|0.184|0.223|0.062|0.862|0.907|0.926\nDFF-DFV|DFF|0.186|0.250|0.062|$\\underline{0.871}$|0.906|0.923\nDDFS|DFF|0.244|0.387|0.109|0.723|0.804|0.849\nDA V2|Mono|$\\textbf{0.063}$|$\\underline{0.089}$|$\\underline{0.035}$|0.865|$\\underline{0.956}$|$\\textbf{0.989}$\nMetric3D V2|Mono|0.095|0.162|0.062|0.826|0.934|0.973\nDAC|Mono|0.176|0.238|0.115|0.654|0.868|0.947\nOurs|DFF|$\\underline{0.068}$|$\\textbf{0.077}$|$\\textbf{0.028}$|$\\textbf{0.919}$|$\\textbf{0.972}$|$\\underline{0.987}$\n## Sintel-Dr. Bokeh\nMethod|Type|RMSE(↓)|AbsRel(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.209|0.797|0.192|0.412|0.644|0.728\nDFF-FV|DFF|0.160|0.661|$\\underline{0.109}$|$\\underline{0.766}$|$\\underline{0.863}$|0.898\nDFF-DFV|DFF|$\\underline{0.134}$|0.569|$\\underline{0.109}$|0.738|0.861|$\\underline{0.907}$\nDDFS|DFF|0.282|1.072|0.282|0.441|0.578|0.648\nDA V2|Mono|0.297|0.482|0.361|0.330|0.419|0.472\nMetric3D V2|Mono|0.170|$\\underline{0.479}$|0.174|0.452|0.561|0.754\nDAC|Mono|0.273|0.951|0.289|0.268|0.409|0.573\nOurs|DFF|$\\textbf{0.095}$|$\\textbf{0.141}$|$\\textbf{0.072}$|$\\textbf{0.806}$|$\\textbf{0.901}$|$\\textbf{0.945}$\n# Table R2: Quantitative comparisons of zero-shot metric depth estimation. \n## Blender-Syn\nMethod|Type|RMSE(↓)|RMSE log(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.425|0.783|0.292|0.135|0.391|0.608\nDFF-FV|DFF|0.325|0.661|$\\underline{0.162}$|$\\underline{0.669}$|$\\underline{0.732}$|0.775\nDFF-DFV|DFF|0.369|0.710|0.196|0.651|0.681|0.707\nDDFS|DFF|0.495|1.120|0.377|0.287|0.361|0.448\nDA V2|Mono|0.725|1.913|0.783|0.018|0.057|0.108\nMetric3D V2|Mono|$\\underline{0.294}$|$\\underline{0.535}$|0.207|0.343|0.625|$\\underline{0.777}$\nDAC|Mono|0.652|1.488|0.594|0.075|0.142|0.198\nOurs|DFF|$\\textbf{0.148}$|$\\textbf{0.282}$|$\\textbf{0.081}$|$\\textbf{0.697}$|$\\textbf{0.878}$|$\\textbf{0.944}$\n## Sintel-Dr. Bokeh\nMethod|Type|RMSE(↓)|RMSE log(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.518|1.504|0.585|0.123|0.204|0.275\nDFF-FV|DFF|$\\underline{0.267}$|$\\underline{0.982}$|$\\underline{0.343}$|0.177|0.364|$\\underline{0.518}$\nDFF-DFV|DFF|0.270|1.038|0.366|0.192|0.332|0.495\nDDFS|DFF|0.706|1.852|0.726|0.203|0.231|0.251\nDA V2|Mono|0.337|1.048|0.396|0.242|$\\underline{0.391}$|0.461\nMetric3D V2|Mono|0.322|1.174|0.469|$\\underline{0.262}$|0.369|0.466\nDAC|Mono|0.515|1.474|0.581|0.144|0.228|0.285\nOurs|DFF|$\\textbf{0.233}$|$\\textbf{0.685}$|$\\textbf{0.253}$|$\\textbf{0.333}$|$\\textbf{0.466}$|$\\textbf{0.560}$\n> **W3.1**: *There are no quantitative results reported for the 4DLFD-Semi-Real setting or the proposed dataset.*\n\nWe now provide the **quantitative results** on the 4DLFD-Semi-Real dataset in the following table. Here we convert all disparity predictions into depth values with unified curves for fair comparisons. It can be found that our method outperforms others in all metrics.\n# Table R3: Quantitative comparisons of zero-shot metric depth estimation on 4DLFD-Semi-Real. \nMethod|Type|RMSE(↓)|RMSE log(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDFF-FV|DFF|1.979|0.198|0.070|0.680|0.888|0.949\nDFF-DFV|DFF|1.943|0.186|0.064|0.711|0.902|0.953\nDDFS|DFF|$\\underline{1.680}$|$\\underline{0.167}$|$\\underline{0.060}$|$\\underline{0.772}$|$\\underline{0.918}$|$\\underline{0.956}$\nMetric3D V2|Mono|2.972|0.221|0.085|0.594|0.892|0.953\nDAC|Mono|2.915|0.229|0.087|0.590|0.889|0.953\nOurs|DFF|$\\textbf{1.549}$|$\\textbf{0.128}$|$\\textbf{0.047}$|$\\textbf{0.832}$|$\\textbf{0.957}$|$\\textbf{0.958}$\n\nFor EDFV-Real, due to the absence of a depth sensor in our current system setup, we were unable to collect ground-truth depth maps for quantitative evaluation. Please refer to Fig. 5 of our paper and Fig. 12 of Supplementary Material for qualitative comparisons. In the future work, we aim to enhance our hardware system by integrating a depth sensor for accurate ground-truth depth capture, enabling quantitative analysis.\n> **W3.2**: *The set of comparison models does not include the event-based DFF methods mentioned in the Related Work section.*\n\nThank you for raising this point. The event-based DFF method mentioned in the Related Work section [2] could only predict sparse depth maps. We have contacted its authors, but their code is not yet ready for public release. Besides, we have also found a recent relevent method [3] published in WACV 2025, which leverages existing grayscale video reconstruction approaches for event-based depth estimation. Their method lacks the input image modality, which could constrain their zero-shot prediction performance. Their code is also not released yet, and our email is not responded as of the rebuttal deadline. We will add the comparisons of these methods as soon as they become available before camera ready if accepted.\n\n[1] Guo et al. Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera. CVPR 2025.\n\n[2] Jiang et al.  Learning depth from focus with event focal stack. ISJ 2024.\n\n[3] Horikawa et al. Dense Depth from Event Focal Stack. WACV 2025."}, {"review_id": "oJsGHwHEDy", "reviewer": "Reviewer_1JE8", "summary": "This paper proposes a novel approach for dense metric depth estimation. In details, the proposed approach intergrades the advantages from both event and RGB images to boost the performance of dense metric depth estimation. The event images are sent to EDFV encoder to obatin EDFV features to obtain sparse depth, while the single view RGB images are passed to a pre-trained image foundation model to get the initial dense depth prediction. A sparseNet and a DenseNet are further used to reflect both outputs into feature space. FInally, a Multi-scale Cross-attention-guided Prompting Module is introudced to fuse the image and event features to obtain the final prediction results. To evaluate the proposed methods, the authors further generate and collect two synthetic datasets and two real-world datasets, and propose the first benchmark for the proposed datasets.", "questions": "1. The authors should highlight their contributions on in the framework, as it currently appears that they have merely assembled a set of existing modules.\n2. The authors should discuss the implications of using a foundation model, such as the fairness of comparisons with other baselines and the potential risk of information leakage in the zero-shot setting.", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Pro:\n1. The proposed idea is very interesting and insightful. This pioneering work can serve as an excellent inspiration for the community.\n2. The authors conduct extensive experiments to prove the effectiveness of the proposed methods.\n3. The newly collected dataset would be a big contribution for the development of the focused research direction.\n\nCons:\n1. Although the proposed idea is very interesting, the proposed framework seems lack of novelty. The main part of the proposed EDPN is the fusion model, which utlizes a cross attention block and a fusion block. However, both cross-attention fusion and conv fusion are all widely used methods in the field of AI. The rest of the proposed framework are all directly rely on existing methods.\n2. Different from existing methods, the proposed approach utlizes a pre-trained image foundation model as the image encoder, which seems unfair compared to the other methods. The authors should prove the performance gain is not caused by this.\n3. The authors use a pre-trained visual foundation model in zero-shot learning task which may causes information leak. The improvment may leads by this.\n\nOverall, I believe this paper is more suitable for the Datasets & Benchmarks track rather than the Main track.", "was_revised": true, "final_justification": "Before rebuttal, there are two mainly concerns about my score: 1) The novelty of the proposed method. 2) Experimental setting and information leakage. In the rebuttal, the authors highlight their contributions with more details, which convinces me that their framework design is indeed novel enough for publication. And additional experimental results have also alleviated my concerns about the unfair comparison, so I have revised my rating accordingly. However, regarding the potential information leakage in the zero-shot setting, since I am not particularly familiar with this area and had explicitly indicated a confidence score of 2, I cannot be fully certain about the authors' response. Therefore, I ultimately gave a rating of 4.", "rebuttal": "We sincerely thank you for your valuable comments and constructive suggestions! We are encouraged that you find our idea insightful, our experiments extensive and our dataset valuable for the community. Below, we will address your proposed Weaknesses (**W**) and Questions (**Q**) in detail.\n> **W1**: *The proposed framework seems lack of novelty. The main part of the proposed EDPN is the fusion model, which utlizes a cross attention block and a fusion block. However, both cross-attention fusion and conv fusion are all widely used methods in the field of AI. The rest of the proposed framework are all directly rely on existing methods.*\n\nWe appreciate your feedback and understand this concern. We respectfully argue that the novelty of our work should be viewed from the **holistic** perspective of the proposed framework (please refer to the proposed second contribution in our paper L63-65). We are the **first** to introduce an EDFV-based prompting method for depth estimation to tackle the problem of scale ambiguities and visual illusions in image foundation models, and experimental results (Sec. 5.3 and 5.4 of our main paper and Sec. E of Supplementary Material) have shown our superiority against foundation models and conventional DFF methods. Each component in the pipeline is essential to the overall method (as discussed in **Ablation studies** in Supplementary Material Sec. D).  \n\nWhile a single component may seem straightforward, our combined pipeline introduces a cohesive and tailored framework that enables effective prompting in a challenging hybrid modality. As discussed in the **Introduction (L48-50)**, the information given by event-based sensors is **different** from other sensors such as traiditional RGB sensors and LiDAR, as it mainly triggers at texture and edge regions. This poses unique challenges and opportunities for our design. In our pipeline, we first utilize proposed EDFV to process events into depth map. After that, we design a SAME strategy to extract effective information from events (**L179-181** of our paper). Besides, considering the modality discrepencies between events and image, we employ different layers to capture their features respectively (**L199-201** of our paper). We also introduce deformable convolutions to handle the sparsity and possible misalignments between two modalities (**L207-209** of our paper). While we agree that the cross-attention fusion and conv fusion may be widely used methods in the field of AI, our adaptation of these modules within a prompting framework tailored for event-based DFF is novel in its use case and validated through extensive experiments. **We will revise the paper to clarify this contribution and better differentiate it from related works.**\n\n> **W2**: *The proposed approach utlizes a pre-trained image foundation model as the image encoder, which seems unfair compared to the other methods.  The authors should prove the performance gain is not caused by this.*\n\nThank you for raising this point. Although other traditional DFF methods (including DefocusNet [3], DFF-FV [4], DFF-DFV [4], and DDFS [5]) **do not** utilize existing image foundation models, Depth Anything V2 [1] and Metric3D V2 [2] **are foundation models themselves**. The results of DA V2 in Table 1&2 of main paper are finetuned from its pretrained ViT-L checkpoint, while ours **also** utilizes the pretrained DA V2 ViT-L checkpoint as image encoder. Therefore, the performance gain of ours against the finetuned DA V2 should **not** be attributed to the pretrained foundation model, but rather the adaptation of the proposed EDFV and prompting framework.\n\nBesides, in order to exclude the influence of pretrained checkpoints, we further conduct ablation studies on the Blender-Syn dataset. We train DA V2 from scratch on the Blender-Syn dataset **without any pretrained checkpoint**. Then we adopt this DA V2 as the image encoder to train our own framework. The results are shown in the following table, where DA V2 trained from scratch is denoted as \"DA V2*\", and our method trained with this scheme is denoted as \"Ours*\". It can be found that even without any pretrained checkpoint, our method still outperforms most of the existing DFF methods in most metrics. Therefore, we believe that the performance gain is **not** caused solely by pretrained image foundation models.\n# Table R1: Quantitative comparisons of in-domain metric depth estimation. \n## Blender-Syn\nMethod|Type|RMSE(↓)|AbsRel(↓)|log10(↓)|$\\delta_1$(↑)|$\\delta_2$(↑)|$\\delta_3$(↑)\n-|-|-|-|-|-|-|-\nDefocusNet|DFF|0.243|0.372|0.107|0.734|0.818|0.861\nDFF-FV|DFF|0.184|0.223|0.062|0.862|0.907|0.926\nDFF-DFV|DFF|0.186|0.250|0.062|$\\underline{0.871}$|0.906|0.923\nDDFS|DFF|0.244|0.387|0.109|0.723|0.804|0.849\nDA V2*|Mono|0.149|0.301|0.085|0.738|0.847|0.905\nDA V2|Mono|$\\textbf{0.063}$|$\\underline{0.089}$|$\\underline{0.035}$|0.865|$\\underline{0.956}$|$\\textbf{0.989}$\nMetric3D V2|Mono|0.095|0.162|0.062|0.826|0.934|0.973\nOurs*|DFF|0.126|0.223|0.058|0.864|0.914|0.938\nOurs|DFF|$\\underline{0.068}$|$\\textbf{0.077}$|$\\textbf{0.028}$|$\\textbf{0.919}$|$\\textbf{0.972}$|$\\underline{0.987}$\n\nOne of our contributions is to fuse image foundation models with event-based DFF through our EDFV-based prompting strategy. Therefore, from the comparison with the image foundation models (DA V2 [1] and Metric3D V2 [2]), we demonstrate that our strategy boosts performance, even outperforming these advanced pretrained models. This shows the benefit of our proposed EDFV and fusion framework, beyond the strength of the encoder itself.\n\n> **W3**: *The authors use a pre-trained visual foundation model in zero-shot learning task which may causes information leak.*\n\nThank you for this important concern. We will address this concern in the following two aspects:\n\n- We have carefully verified the training datasets of our utilized pretrained foundation models, including DA V2 [1] and Metric3D V2 [2], and confirm that **none** of the image datasets used in our evaluations — specifically Sintel and 4D Light Field Dataset — were included in their training corpora.\n\n- We validate generalization performance on **our captured EDFV-real** dataset, as shown in Fig. 5 of main paper and Fig. 12 of Supplementary Material. These results corroborate that our framework performs well in real, unseen scenarios, which could not be attributed to information leakage of foundation models.\n\n> **W4**: *This paper is more suitable for the Datasets & Benchmarks track rather than the Main track.*\n\nWe respectfully argue that although the constructed datasets are one of the contributions of our paper, the proposed EDFV representation as well as the prompting framework are also our key contributions. **Reviewer CTBx** has highlighted the novelty of EDFV, and **Reviewer kC23** has emphasized our innovative framework. As far as we know, we are the first work to solve event-based DFF with prompting approach. Please refer to our responses in **W1** for our contributions to the pipeline. Thanks to our EDFV and prompting framework, we could achieve better results than the existing foundation models on both synthetic and real-world datasets (please refer to Sec. 5.3 and 5.4 of our main paper). Therefore, we believe this work aligns well with the **Main track**.\n\n> **Q1**: *The authors should highlight their contributions on in the framework, as it currently appears that they have merely assembled a set of existing modules.*\n\nPlease refer to our responses in **W1** and **W4**, where we elaborate on the integration of EDFV, modality-aware fusion, and task-specific prompting — together forming a task-tailored framework.\n\n> **Q2**: *The authors should discuss the implications of using a foundation model, such as the fairness of comparisons with other baselines and the potential risk of information leakage in the zero-shot setting.*\n\nPlease refer to our detailed discussions in **W2** and **W3** for the comparison fairness and information leakage.\n\n[1] Yang et al. Depth Anything V2. NeurIPS 2024.\n\n[2] Hu et al. Metric3d V2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation. TPAMI 2024.\n\n[3] Maximov et al.  Focus on defocus: Bridging the synthetic to real domain gap for depth estimation. CVPR 2020.\n\n[4] Yang et al. Deep Depth from Focus with Differential Focus Volume. CVPR 2022.\n\n[5] Fujimura et al.  Deep depth from focal stack with defocus model for camera-setting invariance. IJCV 2023."}]

Author rebuttal:

Meta-review: A paper introduces a metric depth estimation approach by fusing events with image foundation models via a prompting approach.
The paper received **three borderline accept** and **one borderline rejects**.

**Strengths**

- (CTBx,1JE8) The method is well-motivated and carefully designed
- (hHyQ,kC23,1JE8) Proposing a new dataset, good empirical results 

**Weaknesses**
- (CTBx,hHyQ,kC23) Clarity on technical details (ablation study on hyperparameters, limitations, details of data construction)
   - (resolved) partially addressed in the rebuttal (reviewer kC23 disagrees)
- (CTBx) Unclear real-world applicability due to the hardware complexity of the event-RGB system
   - (resolved) Addressed in the rebuttal.
- (CTBx,1JE8) Not novel ideas (eg., prompting image foundation model with sparse depth.)
   - (resolved) Rewriting to differentiate the paper from related work
- (hHyQ) Evaluation on 4DLFD-Semi-Real and EDFV-Real datasets
   - (resolved) Rebuttal included the evaluation

All reviewers confirm that the issues or concerns were resolved in the rebuttal. Reviewer kC23 said the concerns are still remaining after the rebuttal. After carefully checking it, the concerns about additional quantitative results, comparison with recent methods, writing, etc, are resolved. It is highly recommended to accommodate all the discussions and analyses in the final version.

**AC recommends accepting the paper.**
Decision: Accept (poster)