Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining
Authors: Wonhyeok Choi
Kyumin Hwang
Wei Peng
Minwoo Choi
Sunghoon Im
Venue: ICLR 2025 Poster (2025)
Keywords: Self-supervised Monocular Depth Estimation
Deep Metric Learning
Knowledge Distillation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a reflective-aware triplet loss and multi-teacher knowledge distillation, improving depth accuracy on reflective surfaces.

Abstract: Self-supervised monocular depth estimation (SSMDE) aims to predict the dense depth map of a monocular image, by learning depth from RGB image sequences, eliminating the need for ground-truth depth labels.
Although this approach simplifies data acquisition compared to supervised methods, it struggles with reflective surfaces, as they violate the assumptions of Lambertian reflectance, leading to inaccurate training on such surfaces.
To tackle this problem, we propose a novel training strategy for an SSMDE by leveraging triplet mining to pinpoint reflective regions at the pixel level, guided by the camera geometry between different viewpoints.
The proposed reflection-aware triplet mining loss specifically penalizes the inappropriate photometric error minimization on the localized reflective regions while preserving depth accuracy on non-reflective areas.
We also incorporate a reflection-aware knowledge distillation method that enables a student model to selectively learn the pixel-level knowledge from reflective and non-reflective regions. This results in robust depth estimation across areas.
Evaluation results on multiple datasets demonstrate that our method effectively enhances depth quality on reflective surfaces and outperforms state-of-the-art SSMDE baselines.

Published as a conference paper at ICLR 2025
SELF-SUPERVISED MONOCULAR DEPTH ESTIMATION ROBUST TO REFLECTIVE SURFACE LEVERAGED BY TRIPLET MINING
Wonhyeok Choi-.,, Kyumin Hwang".; Wei Peng", Minwoo Choi', Sunghoon Im+.7 Electrical Engineering and Computer Science', Psychiatry and Behavioral Sciences?
Daegu Gyeongbuk Institute of Science and Technology Stanford University?
South Korea', USA
(smu06117, kyumin, subminu, sunghoonim)@dde Edgi ac..kissst............. xepeng@staafford.edi
ABSTRACT
Self-supervised monocular depth estimation (SSMDE) aims to predict the dense depth map of monocular image, by learning depth from RGB image sequences, eliminating the need for ground-trun depth labels. Although this approach simpli- fies data acquisition compared to supervised methods, it struggles with reflective surfaces, they violate the assumptions of I Lambertian reflectance, leading to in- accurate training on such surfaces. To tackle this problem, we propose a novel training strategy for SSMDE by leveraging triplet mining pinpoint reflec- tive regions at the pixel level, guided by the camera geometry between different viewpoints. The proposed reflection-ane -warre triplet mining loss specifically penal- izes the inappropriate photometric error minimization on the localized reflective regions while preserving depth accuracy in non-reflective areas. We also incorpo- rate a reflection-aware knowledge distillation method that enables a student model selectively learn the pixel-level knowledge from reflective and non-reflective re- gions. This results in robust depth estimation across areas. Evaluation results on multiple datasets demonstrate that our method effectively enhances depth quality reflective surfaces and outperforms state-of-th tee--o-thh-rrr art SSMDE baselines.
INTRODUCTION
task that learns depth solely from a continuous RGB image sequence without needing corresponding ground-truth depth maps for each frame in video. This approach significantly simplifies data acquisition com- pared to traditional supervised methods (Fu et al.) 2018 Lee et al., 2019, Bhat et al. 2021), which often involve high costs for annotation. As such, many SSMDE studies (Godard et al. 2019 Zhou
Self-supervised monocular depth estimation (SSMDE) (Godard al., 2019)
al. 2017] Garg et al., 2016 Guizilini et al. 2020) have explored its viability as mainstay for applications such as autonomous driving, highlighting its potential in outdoor environments.
Despite its advantages, SSMDE approaches typically challenge in accurate depth estimation non- Lambertian surfaces such as mirrors, transparent objects, and specular surfaces. This difficulty pri- marily arises from the assumption of Lambertian reflectance (Basri & Jacobs (2033) embedded in most SSMDE methods. As illustrated in Figure [ these non-Lambertian surfaces violate the pho- tometric constancy principle, which posits that the color and brightness of point should appear constant across different images (Godard et al.) (017). This violation leads to incorrect depth train- ing, particularly on Lamberrinnn surfaces. Consequently, this issue manifests in a phenomenon known as the "black-hole effect" (Shi et al. (202)), where the model erroncously predicts depths that are greater than the actual surface depth in areas with specular reflections. This effect a prevalent challenge across various reflective surfaces, significantly impacting the performance and reliability of SSMDE systems.
'These authors contributed equally. SS. Im the corresponding author.

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Published as a conference paper at ICLR 2025
Come
Lye
Comy
souree
projected suriice point (non reflective)
Incident light
Reflection dependeat)
Com
@VB
prjjecal surface psint (ceflective)
nen-Refleative
lnf
(O) (x)
ReCieiivee (Lamiertian)
Reflective
Coo
Warp
O L2 (n(()) (x)
Comp
Incident
Tyc
Trof
1x2r
(x())) (()
Com:
light
Vinlating Photomatric Constancy Principle!
Camtro
Comid
Reflective Lam-Laambertian)
Figure 1: Photometric constancy violation reflective surfaces. The projected non-reflective sur- face point (denoted as O) satisfies the photometric constancy so the model can obtain the accurate depth by photometric eπror minimization. On the other hand, projected reflective surface point (de- noted as O,O) violates the photometric constancy, resulting in wrong disparity by photometric error minimization. This figure depicts scenario where the relative positions of the cameras shift hori- zontally, akin to rectified stereo, to simplify the illustration.
Recent advancements (Costanzino al. 2023 Shi et al. 2023 attempt tackle these challenges
by utilizing training strategies that involve generating pseudo-labels through inpainting (Costanzino) al. 2023) reconstructing 3D meshes (Shi et al. 2023). However, these methods still rely on extra labels such as segmentation mask annotations (Costanzino et al. (023) or use auxiliary methods that have excessive computational costs such as ensemble- based uncertainty algorithms (Shi al. 2023
TSDF- fusion (Newcombe et al. 2011) and mesh rendering (Matl 2019).
To address these issues, we propose novel training strategy called "reflection-awar triplet min- ing" that enhances the performance of SSMDEs by leveraging the triplet mining (Schroff et al.) 2015). The underlying principle of our approach that reflective areas, such as mirror light sources or objects, exhibit disparities corresponding to the reflected object rather than the actual surface as illustrated in Figure [ While non-reflective areas exhibit photometric error due to the difference in camera views from two different perspectives (e.g., source and reference views), reflective areas have abnormally low photometric error between the two views due to the low disparity of reflected objects. Accordingly, our approach treats views from the same camera coordinates as positive pairs and those from different coordinates as negative pairs, as illustrated in Figure 2 Our method aims minimize the conventional photometric error between positive pairs while maximizing it between negative pairs. This approach effectively neutralizes the impact of contaminated gradients in reflec- tive regions, thereby significantly improving accuracy on these regions.
Moreover, we introduce a "reflection-aware knowledge distillation" approach to keep the high- frequency details in the predicted depth for non-reflective regions inspired by previous works (Shi et al. 2023). In this method, the student network is trained by distilling knowledge from two distinct SSMDE networks. The first utilizes the proposed triplet loss, providing robustness against reflective areas, while the second employs solely photometric minimization loss, adept at preserving high- frequency details that contribute to the perceptual quality and visual fidelity the depth map. This hybrid training strategy effectively combines the strengths of both training methods, creating more versatile and effective depth estimation framework. By leveraging the unique capabilities of each model, the student network can achieve more comprehensive understanding of depth across vari- ous surface conditions.
Our method is broadly applicable general SSMDE frameworks that rely photometric error minimization. We validate our method on three well-known SSMDE networks (Godard et al.) 2019
Lyu et al. 2021 Zhao al. 2022) across three public datasets (Dai et al. 2017 Shotton et al.
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Published as a conference paper at ICLR 2025
2013 Ramirez et al. 2023 featuring reflective objects and surfaces. The results demonstrate that our method significantly improves depth prediction accuracy on reflective surfaces while preserving performance on non-reflective surfaces. Our main contributions are fourfold follows:
1. We propose new reflection-aware triplet mining loss that significantly enhances the accuracy on reflective surfaces and can easily integrated into general SSMDE frameworks.
We introduce reflection-awarr knowledge distillation to improve the overall accuracy on reflec- tive surfaces while preserving high-frequency details on non-reflective surfaces.
3. To the best of our knowledge, our strategy represents the first end-to-end method specifically designed to enhance the performances of SSMDE on reflective surfaces.
4. The proposed method outperforms the existing self supervisd training method and shows com- parable results against 3D information distillation methods on various indoor depth benchmarks.
2 RELATED WORK
2.1 SELF-SUPERVISED MONOCULAR DEPTII ESTIMATION
Self-supervised Monocular Depth Estimation (SSMDE) is a task that estimates a depth map from a single image without a ground truth depth map. This approach significantly simplifies the process of data acquisition, making it scalable for a wide variety of datasets. SfMLearne (Zhou et al., 2017) introduces pioneering framework for sulf-super depth map estimation, which simultaneously learns depth maps for the input image and pose parameters from sequential views. Monodepth2 (Go-) dard et al. 2019) proposes a masking scheme and minimum reprojection loss to filter out the regions that violate photometric constancy, such as moving objects and occluded regions. Subsequent meth- ods (Zhou et al.) 2021] Guizilini et al.) 2020, Lyu et al. 2021) have been refined, effectively inte- grating features of different resolutions based on established constraints. With the introduction of VIT (Dosovitskiy et al. 2020), the field of SSMDE has begun to incorporate transformer backbones. Monoformer (Bae et al. (023b) and Mono ViT (Zhao et al., 2022) have emerged, utilizing hybrid networks of CNN and transformers to adeptly merge local and global features.
2.2 GENERALIZATION OF MONOCULAR DEPTH ESTIMATION
Recent research has expanded to consider factors such as the impact of weather variations (Saun- ders et al. 2023 Gasperini et al. 2023). the differences in inference capabilities between CNNs and Transformers (Bae et al. 2023a). the robustness of SSMDEs against various types of data corrup- tion (Kong et al.) 2024), and methods for accurately modeling transparent and mirrored surfaces, which are typically non-Lambertian (Costanzino et al. (003). In addition, the 3D Distillation (Shi let al. 2023) addresses critical flaw in traditional SSMDES: the photometric constancy principle used in applying photometric consistency loss may not hold for non-Lambertian surfaces encoun- tered in real-world scenarios, resulting in SSMDE models producing unreliable and low-quality depth estimates for reflective surfaces. To counter this problem. 3D Distillation leverages the 3D mesh rendering function along with ensemble uncertainty localize the reflective surfaces and re- fine the inaccurate depth on these regions.
2.3 DEEP METRIC LEARNING
Deep metric learning (Chen et al. 2020 Chen & He 2021 Khosla et al.. 2020) seeks to develop an effective distance measure between data points. These methods strive minimize the distance be- tween samples of the same class while maximizing it between samples from different classes. While traditionally focused on classification tasks, where positive and negative pairs are defined based on class similarity, recent studies (Spurr et al.] [021]] Wang et al.) 2022 Zha et al., (024)) have expanded the application of deep metric learning to regression contexts. Particularly in the context of depth estimation, deep metric learning has demonstrated versatility beyond simple augmentation-basse consistency. It has been applied to enhance accuracy through contrasting depth distributions (Fan) et al. 2023 Choi et al.) 2024) and addressing issues such as edge fattening (Chen al. 2023). In this paper, we utilize the triplet mining scheme, initially popularized by |Schroff' et al. (2015), to en- hance recognition accuracy, specifically focusing on improving performance reflective surfaces.
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3 METHOD
Our method aims to enhance depth prediction accuracy reflective surfaces by strategically penal- izing the inappropriate photometric error minimization between the hiw-synthesized image and the reference image. In Section 3.1 we discuss the photometric constancy principle, which posits that correctly minimizing photometric error is crucial for accurately determining depth (Section 3.1.1. We also provide an overview of the standard training strategies employed in SSMDE frameworks (Section 3.1.2). In Section 3.2 we detail the three components of our training strategy: reflective re- gion localization (Section 3.2.1 ). reflection-aware triplet mining loss (Section 3.2.2p. and reflection-
aware knowledge distillation (Section 3.2.3)
3.1 PRELIMINARY
3.1.1 PHOTOMETRIC CONSTANCY PRINCIPLE
The photometric constancy principle is foundational in SSMDE frameworks, positing that surfaces exhibit uniform reflectance (i.e., Lambertian reflectance) from all viewing angles. A surface adheres to this principle if its color and luminance observed through a camera remain constant, regardless of the camera's viewing angle. By leveraging this property, depth and pose can be accurately esti- mated by minimizing the photometric error between the view-synthesized image and the reference image, as described in Equation 3 However, real-world scenes rarely adhere strictly to this princi- ple. Non-Lamber surfaces, such as specular reflections from light sources or mirrored objects, are prevalent, leading to violations of photometric constancy. These deviations result in significant errors when attempting to minimize photometric error, thus compromising the effectiveness of depth estimation methods based on these assumptions.
3.1.2 TRAINING STRATEGY OF GENERAL SSMDE FRAMEWORK
The objective of SSMDE is to predict per-pixel basis depth map Dref of reference image / given the reference image itself, source image Lyve (or source images) and their camera intrinsics K. The framework consists of depth network Fo(.), and a pose network Go(:.)) to respectively estimate the depth of the reference image Dref, and the relative pose [R[t),2 as follows:
Dref = (I(ef)))
Fø B3xxxx + 11xxx
(I)
[ [Rtt),2 = Go Leff),
Go 22x3xhx
+ R3x4
(2)
where (h, w) represent the spatial resolution of Lref. Using the obtained relative pose [R]t] and depth map Dref, the source image Lrre is warped into the reference coordinates, generating the view-synthesized image 1,2r follows:
(Isrr),n. (
K- K 1)
(3)
where (u, v) represent the image coordinates and (.) is the projection function that maps homo- geneous coordinates to image coordinates. By the photometric constancy principle detailed in Sec- 1i.. the synthesized image Iszr should exhibit the same colors and luminances as the reference image pixel-by-pixel basis. Consequently, the model determine the accurate depth and pose by minimizing the photometric errors, P(:, +), between I,2r and Iref as follows:
1 S(Ire// Io2r) + are - Ie2r I02-111): lt):
P(Ierr,Ire MO a1
2
(4)
p 22x3xxxx
+ R xlxxxx
S: 22x3xxxx + 12 xxlxxx
where S(.,.) is the mixture of structural similarity index Wang et al. 2004), and M is the principled mask (Godard al. 2019) to prevent the backpropagation of corrupted gradients, caused by anoma- lies like moving objects in the scene. The weights a1 and ag serve as balance terms between two losses, and @ represents the element-wise multiplication operator. However, if the surface does not conform to the principle of photometric constancy, minimizing photometric errors on such reflective surfaces can lead significant inaccuracies in the estimated depth.
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(x). cross-view warping
(b). photometric error pos. neg. pairs
Lue
Fndf
Emro
time
Callaan leet) Eaalaa
EpalTarr Aen)
Ax
Ax
Waip
Positivee Pair
Jyee
4,2
hair
Negative
Mininie Eyh
Mininino Epa.
x
Pair
Maximine Lph.
122
Figure 2: The effect of the proposed method on reflective/non-rellciiie surfaces. (0/O, imply the projected non-rellectiie/relleetie surface points, respectively, and (Ø, ② denotes the location of reflection lobe in view-synthesized image coordinate. Our proposed method cancels out the wrong photometric error minimization in reflection areas by contrasting the negative pair samples.
3.2
METHODOLOGY
3.2.1 REFLECTIVE REGION LOCALIZATION
The photometric error, calculated through Equation between 1,2r and Iref tends to be smaller in non-reflective regions. This is because these areas reflect consistent colors and luminances ir- respective of the viewing direction, adhering to the photometric constancy principle. On the other hand, reflective regions, which violate the photometric constancy principle, exhibit abnormally low disparities due to the additional distance from the reflected light source. Consequently, as illustrated in Figure 2 reflection lobes from different images appear relatively closer in image coordinates, resulting in reduced photometric errors in the RGB images of two different viewpoints.
This characteristic is crucial for isolating reflective regions within the spatial dimension of the image. To capitalize on this, we first generate view synthesized images Ts2r: Ir2s manner similar to the process outlined in Equation 3
(L2rr). Laro ( ((K[ () (D ref Deef K
[u, v, 1J [u, v, 112)),
(5)
(I 2., I,ey [K[ (D snod K
(6)
where the relative pose [ can be obtained by computing the inverse of the predicted pose [R]t] and Drre is predicted depth from Lore, following procedure similar to Equation Uti- lizing these synthesized cross-view images, we compute two photometric errors measure discrep- ancies between image pairs as follows:
Ex P(I.err.re)) =
= P(1.rr L,2s).
The first error, E+ quantifies discrepancies between images taken from the same viewpoint Iref). This error is minimized when depth and pose are accurately estimated on non-reflective surfaces. Conversely, the second error, E measures discrepancies between images from differ- ent viewpoints (I,2r, I,2s). In general, the expected photometric error for E- should be substantial due the different camera coordinate systems. However, on reflective surfaces, the variations in light reflection may result in a reduced photometric error. Based on these observations, we identify pixel-level reflective regions M,
R 1xhx as follows:
1, if (E))... (E+) (E+)...v S 8,
[M)....
(8)
0.
else,
where 8 is a certain margin that represents the minimum significant photometric difference required to distinguish between the two surface types, where a value of I corresponds to a reflective pixel and 0 to non-reflective pixel, respectively. This method effectively utilizes discrepancies in photometric errors to distinguish between reflective and non-reflective surfaces, providing a precise mapping of surface properties within the image. (refer Section D in the supplementary materials.)
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3.2.2 REFLECTION- AWARE TRIPLET MINING LOSS
We introduce the reflection-aware triplet mining loss, Liri, which addresses the limitations of using E alone in environments where reflections disrupt depth accuracy. In reflective regions, simply minimizing E+ does not effectively discern between real and reflected disparities. To counteract this, we assert that E should be significantly greater than E+ This approach is inspired by triplet mining techniques that aim to minimize the distance within positive pairs and maximize it within negative pairs, enhancing the model's ability to distinguish between reflective and non-reflective surfaces. To implement this, we formulate the reflection-awar triplet mining loss as follows:
Leri(Tref, Is2r, I22.) = M, @ EE+ - + Dinige + (1 M,) C E*,
where (-)hinge is the hinge loss function described in Hearst al. (999). In this configuration, the reflection-awane triplet mining loss is applied specifically to regions identified as reflective. For non-reflective regions, where reflections do not disrupt photometric assessments, we apply the pho- tometric loss E+ as it reliably reflects photometric consistency. This differentiation allows the model address the unique challenges presented by each type of region effectively.
As illustrated in Figure 2 this strategy involves not only penalizing the minimization of E but also actively maximizing E This method effectively counteracts the contaminated gradients typically found in reflective regions. By adjusting the balance between E and E based on the presence of reflective surfaces, our method not only improves depth estimation complex scenarios but also ensures robust performance against the challenges posed by reflective surfaces. This comprehensive approach results in a model that accurately reflects the true topography of both reflective and non- reflective environments.
3.2.3 REFLECTION- KNOWLEDGE DISTILLATION
The proposed end-to-end training scheme described in Section 3.2.2
effectively handles the depth
estimation on both reflective and non non-reflective surfaces. To further refine depth estimation quality, we introduce reflection- aware knowledge distillation strategy inspired by the fusion techniques discussed in Shi et al. (2023), aimed at retaining high-frequency details in depth prediction.
Our approach begins by training two separate SSMDE networks. The first is trained using our reflection- triplet mining loss, Ltri, as defined in Equation 9 and the second employs the conventional photometric loss, E+ From these models, we generate two types of depth maps: Deris derived from the reflection-aware model, and Dori, obtained from the model trained with conven- tional photometric loss. We then merge these depth maps into a single pseudo depth map D Dpee utilizing reflective region mask M,. This mask facilitates the adaptive fusing of depth information from both teacher models based on the presence of reflective properties in the image. The pseudo depth map generation and distillation process is detailed in the following equation:
Lyked Dpse) ) log D log D Ppeel: where D pse M, + Deri + (1 M,,) C Doris
(10)
where D represents the depth predicted by the student model. It is important to note that the student model and the two teacher models share the same network architecture the general SSMDES. This structured training approach not only addresses the specific challenges posed by reflective surfaces but also ensures that the tigh-frequency detail is not lost, thus achieving a balanced and accurate depth prediction across different surface types.
EXPERIMENTS
Datasets. ScanNet (v2) DDai et al. 2017 is comprehensive indoor RGB-D video dataset com- prising 2.7 million images across 1,216 interior scene sequences. Traditionally, this dataset has been pivotal for evaluating multi-view stereo (Im et al. 2019 Bae et al. 2022) and scene reconstruction applications (Murez et al. 2020 Zhou et al. 2024 KITTI (Geiger et al. (013)) captures autonomous and
driving information from outdoor scenes via cameras and lidar sensors and is the most representative
benchmark for depth estimation research. NYU-r2 Silberman et al. 2012) serves as one of the most established and widely used benchmarks for depth estimation, as it provides a diverse set of indoor scenes with varying lighting conditions and wide variety of objects, captured

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Published as a conference paper at ICLR 2025
Table 1: Main results on the ScanNet-Reflettion Test and Validation sets.
Backbonc
Training Scheme
Method
Rel
Sq
RMSE log
6- 1:25
11225
6 1:25'
End-ro-End
Self-Supervised
0.181
0.160
0.521
0.221
0.758
0.932
0.976
Ours
0.157
0.096
0.468
0.201
0.762
0.949
0.988
ScannNet- ScanNet-Relleetie Relllctton Test
Monodepth2
Self-Teaching
0.179
0.502
Multi-Stge
Distillation
0.156
0.459
0.766
0.945
0.958
Ours'
0.150
0.087
01.466
0.192
0.777
0,955
0.990
End-to-End
Sell-Supervised
0.182
0.530
Ours
0.157
0.098
0.470
0.201
0.763
0.952
0.989
HRDepphh
Self Teaching
0.175
0.145
0.492
0.215
0.757
0.936
0.982
Multi-Stuge
3D Distillation
0.152
0.089
0.451
0,190
0.771
0.956
0.990
Ours!
0.150
0.092
0.434
0.192
0.780
0.950
0.988
Endito- End
Ourx
0.154
0.136
0.087
129
0.414
0.458
0.178
0.197
0.822
0.955
0.967
0.988
0.979
Moooo VIT
Self SelfTeaching
0.151
0.130
0.139
0.191
0.837
0,950
0.978
Multi-Stge
3D Distillation
0.127
0.069
0,379
0.162
0.846
0.961
0.992
0.126
0.395
Supervisd
0.206
0.912
0,961
End-to-End
Owrs
0.166
0.125
0.492
0.209
0.763
0.934
0
ScauNef- R-Rlllection Validation
Monodepth2
Self: Self-Teaching
0.192
0. 188
0.548
0.233
0,764
0.920
0.967
Multi-Stage
Distillation
0.156
0.943
Owrs
0.151
0.105
0.454
0.193
0.796
0.944
0.945
Ead-to-End
Self Supervised
Ours
0.213
0.167
0.244
0.605
0.496
0.255
0,741
0.906
0
HRDepth
Self: Teaching
0.202
0.208
0.565
0.243
0.756
0.914
0.964
Multi-Stage
3D Distillation
0.153
0.090
01,400
0.188
0,789
0.948
0.989
0.151
0.104
0.450
0.949
0.987
End to End
Self-Supervised
0.179
0.206
0.557
0.227
0.819
0.930
0.963
Ours
0.139
0.107
0,452
0.183
0,836
0,954
0,984
MonoViT
Sell-Tenching
0.176
0.537
0,966
Multi Stage
3D Distillation
0.126
0.068
0.367
0.159
0.851
0.965
0.991
Ours!
0.130
0
0,420
0,173
0,851
0,960
0,987
using a Kinect sensor. 7-Scenes (Shotton al., 2013) is challenging RGB-D dataset captured in in- door scenes with similar distribution to ScanNei but dominated by non-reflective surfaces. We fol- low the evaluation protocol of Long et al. (202T); Bae et al. (2022) to demonstrate the cross-dataset generalization performances. Booster (Ramirez al. (2023) includes variety of non-Lambertian objects within indoor settings, such as transparent basins, mirrors, and specular surfaces. Following the training split our test set, which showcases method's
the Costanzino al. (2023), , adaptability to more complex scenes.
Training scenario. In the work of 3D distillation (Shi et al. 2023), the ScanNet dataset has been further segmented into ScanNet-Refleee ence reflective objects within the scenes. This subdivision results in a ScanNet-Refleccte consisting of 45,539 training, 439 validation, and 121 testing samples. Additionally, ScanNet- NoReflection validation set comprising 1,012 samples evaluates the model's generalization when trained in reflective environments. Aligning with these methodologies, the training process lever- ages the CcanNet-RRffectiio train set to simulate real-world scenarios involving reflective surfaces. For the KITTI and NYU-v2 experimental setups, we follow the training protocol of Godard et al.) (201)) incorporating our reflection- triplet loss and distillation training procedure. All exper- iments conducted on KITTI and NYU-v2 utilized a ResNet-18 pose network instead of a GT pose.
and CeanNet-NoReflectin
subsets based on the pres- dataset
Evaluation. For guantitative evaluations, we employ standard metries from the depth estimation literature (Eigen et al., 2014 Geiger et 2012). We differentiate our training approaches into end- to-end and multi-stage (distillation) strategies to effectively assess the models. The model trained solely using reflection-aware triplet mining loss Liri, referred to as "Ours", and another utilizing the proposed distillation method Lykd. referred to as "Ours!" are evaluated under their respec- tive conditions. We compare these against both end-to-end and multi-stage baselines across three sets: CranNeetRRffectiin TTest, Validation } sets, and ScanNet- NoReflection Validation set. For the KITTI (Geiger et al. 2013) and NYU-v2 (Silberman al.) 2012) datasets, follow the standard evaluation protocol of Godard et al.] (2019). To underline the cross datsset generalizability of our methods, we also perform zero-shot evaluations the 7-Scenes and Booster.

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Published as a conference paper at ICLR 2025
Table 2: Main results on the ScanNat-VVRefleccion Validation set.
Backbone
Training Scheme
Mothod
Abs Rel
Rcl
RMSE
d
1.25
End-to-End
Selt-Supervised
0.169
0.100
0.395
0,206
0.759
0,932
0.979
Ours
0.168
0.095
0.395
0.208
0.751
0,931
0,980
Monodopth2
Self- SellhTeahing
0.161
0.090
0.375
0.196
0.777
0.939
0.981
Multi Stage
3D Distillation
0.159
0.087
0.373
0.195
0,779
0.941
0,983
0.085
0.195
0.942
0,983
End-to-End
Self-Supervice
0.169
0.096
0.102
0.388
0.202
0.204
0.766
0,933
0.933
0.979
0,980
HRDDepth
Self-Teaching
0.160
0.089
0.367
0.192
0.784
0.941
0. 0.982
Multi
3D Distillation
0.158
0,086
0.365
0.190
0,786
0,942
0,983
Own!!
0.157
0.086
0.366
0.192
0.784
0.942
0,983
Ead-to-End
Self Supervised
Oury
0.140
0.074
0.333
0.338
0.171
0.174
0.829
0.952
0.952
0,984
0.987
MonoVi7 VIT
sal-Teeahinng
0.134
0.068
0.317
0.164
0.8440
0.956
0.987
Multi-Stage
Distillation
0.065
0.162
0,956
0,987
Owr!
0.133
0.066
0.320
0.166
0.837
0.957
0.987
Implementation details. Our experiments incorporate three leading architectures in SSMDE: Monodepth2 (Godard et al. 2019), HRDepth (Lyu et al., 202I). and Mono ViT (Zhao et al. 2022),
which have demonstrated exceptional performance in previous studies. Each backbone trained by different training schemes, including Self-Supervised (Godard et al., 2019), Self-Teaching (Poggi al. 2020), and 3D Distillation (Shi et al., 2023), compare with method. To align closely with 3D Distillation, all training particulars follow their documented conditions, with adaptations only in our proposed training strategy. Specifically, the models are trained using the reflection triplet split introduced in 3D Distillation. To finely tune the margin 5 across positive and negative pairs, it is adaptively selected based on the difference between the first quartile (Q1) of E+ and the third quartile (Q3) of E
4.1 EVALUATION ON REFLECTION DATASETS
CcanNettRRflecttinn dataset. To demonstrate the effectiveness of the proposed method on reflec- tive surfaces, we conduct a quantitative evaluation using the SeanNet-Reflectine dataset. The eval- uations are divided into end-to-end and multi-stage methodologies. As depicted in Table [ Ours, categorized under end-to-end training schemes, significantly outperforms self-supervised methods across all backbones, achieving Abs Rel average increase of 12.90% in the test split and 21.12% in the validation split. Moreover, it is noteworthy that Ours shows a significant performance boost, with average improvement of 10.75% over Self- Teaching across all metrics in both the test and validation splits, with only two exceptions in 42 metrics (ō < 1.25 of Monodepth2 and MonoViT). This demonstrates that reflection-awarc triplet mining loss is effective in detecting reflective surfaces and encourages the model to obtain accurate depth on these surfaces as shown in Figure 3 Additionally, our multi-stage approach, which employs reflection-aware knowledge distillation (de- noted as Ours!), ), delivers comparable results across all backbone models of 3D Distillation. Note that the proposed method does require complex scene reconstruction procedures such as mesh ren- dering (Matl, [2019] Newcombe et al. 2011) or ensembles of multiple neural network models (Lak- shminarayanan et al. 2017).
CcanNet-NoReflection dataset. Table summarizes the results of quantitative evaluation per- formed on the ScanNet-NoReflection dataset. This evaluation aims to measure the generalization performance of models trained on datasets that include reflective surfaces. an end-to-end training scheme, Ours achieves performance comparable to or within acceptable margin of self-supervised methods. This confirms that our proposed reflection-awarr triplet mining loss effectively prevents the incorrect back-propagation of the photometric loss gradient reflective surfaces, illustrated in Figure| 2 Furthermore, the model trained by our reflection-ave -wware knowledge distillation (i.e., Ours") shows noticeable performance improvement, which is comparable performance the 3D distillation method. These results suggest that extending our reflection-awart aware triplet mining loss to distillation techniques offers a straightforward yet effective strategy for managing reflective surfaces.
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Published as a conference paper at ICLR 2025
RGB
GT Depth
RGB
GT Depth
RGB
GT Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
Self
Sup.
Ours
Ourst
Figure 3: Qualitative results the proposed methods on the ScanNet. We visualize the predicted
depth of the Monodepth2 (Godard al., 2019) trained by three different methods including the proposed method: Self-supervised, Ours and Ours". Note that the error map represents the absolute difference between prediction and ground truth depth, normalized to between and 255.
Table 3: Main results on the KITTI and NYU-v2 datasets.
J
Sq Rel
RMSE
log
5 1.25'
1.25
NYU12 KITII
Self Sul--uupee
0.118
0.196
0.871
0,957
Monodepth2
Ours
0.118
0.912
4,943
0.198
0.867
0.956
0,980
Ours
0.116
0.856
4.796
0.194
0.870
0.958
0.981
Self Supervised
0.171
0.144
0.622
0.213
0.746
0,941
0,985
Menodepth2
Ours
0.166
0.139
0.616
0.209
0.759
0.943
0,985
Ours?
0.155
0.121
0.573
0.196
0.782
0.951
0.988
KITTI and NYU-v2 datasets. Table the quantitative results of the Monodepth2 archi- tecture on the KITTI NYU-v2 datasets, both widely used for depth estimation. the KITTI dataset, most scenes share similar attributes and predominantly consist of non-reflective objects, limiting performance improvements
reflective regions. However, as highlighted our study, selective triplet mining loss using M, and the distillation process effectively preserves per- formance in non-reflective areas. Consequently, our method achieves performance comparable to existing approaches with negligible margins. These results consistent with the trends observed in the CcanNet-NoRRflectinn
validation split experiments presented in Table 2 On the other hand, in the NYU-v2 dataset, where reflections are more prevalent, our triplet mining loss and distillation method yield significant performance gains. These results demonstrate that the broad applicability of our approach to indoor environments with complex light-object interactions, ensuring consistent enhancements even when incorporating a pose network.
4.2 CROSS-DATASET GENERALIZABILITY
To demonstrate the generalization ability across different datasets, we conduct zero-shot evalu- ation using 7-Scenes and Booster datasets. As shown in Table 4 our proposed methods (denoted as Ours and Ours consistently enhances performance. Specifically, across all backbone architec- tures and all metrics, Ourst improved by average of 5.47% and 13.89% for the 7-Scenes and Booster datasets, respectively. Exceptionally, there is no significant difference between Ours and the self-supervised method the 7-Scenes dataset. This may be attributed the predominance of

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Published as a conference paper at ICLR 2025
Table Cross-dataset evaluation result on the 7-Scenes and Booster datasets.
Backbone
Method
Abs Rel 4
Sq. Rel
RMSE
RMSE log
1.25
1
5 < 1.25"
1.25
Supe-Super
0.210
0.445
0.248
0.656
0,906
Monodepth2
Ours
0.207
0.125
0.441
0.248
0.656
0.904
0,975
Ours!
0.198
0.110
0.415
0.238
0.667
0.911
0,980
7-Scenes
Self-Supervised
0.193
0.115
0.421
0.231
0.682
0.921
0,982
HRDehh
0.195
0.232
0.674
Ours!
0.183
0.096
0.389
0.219
0.706
0.931
0,986
Self-Supervised
0.173
0.093
0.365
0.201
0.752
0.945
0,988
Mono
Ours
0.175
0.090
0.361
0.204
0.746
0.944
0.987
Ours!
0.162
0.077
0.335
0.191
0.776
0.951
Self-Superviser
0.520
0.429
0.601
0,444
0.305
0.591
0.827
Monodepth2
Ours
0.430
0.301
0.501
0.389
0.362
01675
0.893
Ours!
0.419
0.381
0.370
Booster
Self-Supervised
0.495
0.391
0.559
0.426
0.307
0.611
0.852
HRDepth
Ours
0.414
0.276
0,482
0.379
0.364
0,680
0,907
Owrs
0.429
0.292
0.487
0,385
0.366
0.639
0.878
Self-Supervised
0.418
0.327
0.504
0.374
0.425
0.679
0.888
MonoVIT
Ours
0.408
0.302
0.482
0.362
0.414
0.677
0,916
Ours!
0.375
0.249
0.440
0.337
0.422
0.734
0.944
non-reflective surfaces in the 7-Scenes dataset, where our model, trained with the reflection-aware triplet mining loss, slightly loses high-frequency details on non-reflective surfaces. Conversely, the consistent performance improvement of Ours' across both reflective and non-reflective surfaces demonstrates the robust generalization capabilities of our method based reflective region selec- tion.
5 CONCLUSION
This paper addresses the intricate challenge of self-supervised monocular depth estimation on reflec- tive surfaces. Our method employs novel metric learning approach, centered around reflection- aware triplet mining loss. This novel loss function significantly improves depth prediction aceu- racy by accurately identifying reflective regions minimization of photometric errors, which are typically problematic on reflective surfaces. It also preserves frequeccy details non-reflective surfaces by selectively regulating photometric ror minimization based on reflection region selection. Moreover, we introduce a reflection-awwe -wware knowledge distillation method, enabling a student model to enhance performance in both reflective and non-reflective surfaces. This method leverages the strengths of different teaching networks to produce more robust and versatile student model. Experimental evaluations conducted the in- door scene datasets demonstrate our method consistently enhances depth performance across various architectural frameworks. These results underscore the robustness and versatility of our approach, marking it as valuable contribution to the field of self-supervised monocular depth estimation.
per-pixel basis and effectively adjusting the
ACKNOWLEDGEMENT
This work was supported by Korea Research Institute for defense Technology planning and ad- vancement through Defense Innovation Vanguard Enterprise Project, funded by Defense Acquisi- tion Program Administration (R230206) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00210908)
REFERENCES
Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. Multi-view depth estimation by fusing single- view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 2842-2851, 2022.
10

---

Published as a conference paper at ICLR 2025
Jinwoo Bae, Kyumin Hwang, and Sunghoon Im. A study the generality of neural network struc- tures for monocular depth estimation. IEEE Transactions on Pattern Analysis and Machine Intel- ligence, 2023a.
Jinwoo Bae, Sungho Moon, and Sunghoon Im. Deep digging into the generalization of self- supervised monocular depth estimation. In Proceedings of the AAAI conference artificial intelligence, volume 37, pp. 187-196, 2023b.
Ronen Basri and David W Jacobs. Lambertian reflectance and linear subspaces. IEEE transactions on paltern analysis and machine intelligence, 25(2):218-233, 2003.
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka. Adabins: Depth estimation using adap- tive bins. In Proceedings of the IEEE/CVF Conference Computer Vision and Puttern Recogni- tion, pp. 4009-4018, 2021.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. ICML, pp. 1597-1607. PMLR, 2020.
Xingyu Chen, Ruonan Zhang. Ji Jiang, Yan Wang, Ge Li, and Thomas Li. Self-supervised monoc- ular depth estimation: Solving the fatteninnnni problem. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision, pp. 5776-5786 2023.
Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In CVPR, pp- 15750-15758, 2021.
Wonhyeok Choi, Mingyu Shin, and Sunghoon Im. Depth-discriminative metric learning for monoc- ular 3d object detection. Advances in Neural Information Processing Systems, 36, 2024.
Alex Costanzino, Pierluigi Zama Ramirez, Matteo Poggi, Fabio Tosi, Stefano Mattoccia, and Luigi Di Stefano. Learning depth estimation for transparent and mirror surfaces. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9244-9255, 2023.
Angela Dai, Angel X Chang. Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5828-5839, 2017.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenhorn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010. 11929, 2020.
David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from single image using a multi-scale deep network. Advances in neural information processing systems, 27, 2014.
Rizhao Fan, Matteo Poggi, and Stefano Mattoccia. Contrastive learning for depth prediction. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3225- 3236, 2023.
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao. Deep ordinal regression network for monocular depth estimation. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2002-2011, 2018.
Ravi Garg, Vijay Kumar Bg, Gustavo Carneiro, and Ian Reid. Unsupervised enn single view depth estimation: Geometry to the rescue. In Computer Vision-ECCV 2016: 14th European Con- ference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14, pp. 740- 756. Springer, 2016.
Stefano Gasperini, Nils Morbitzer, HyunJun Jung, Nassir Navab, and Federico Tombari. Robust monocular depth estimation under challenging conditions. In Proceedings of the IEEE/CVF in- ternational conference on computer vision, pp. 8177-8186. 2023.
A Geiger, M Nießner, M Pollefeys, Rother, D Scharstein, AA Hassan, A Dai, Honauer, Janai, T Sattler, al. Robust vision challenge. In CVPR Workshop, 2018.
11

---

Published as a conference paper at ICLR 2025
Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pp. 3354 1361. IEEE, 2012.
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1211-1237, 2013.
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow. Unsupervised monocular depth estima- tion with left-right consistency, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 270-279, 2017.
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel Brostow. Digging into self- supervised monocular depth estimation. In Proceedings of the IEEE/CVF international confer- ence on computer vision, pp. 3828-388 3838, 2019.
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 2485-2494, 2020.
Mu He, Le Hui, Yikai Bian, Jian Ren, Jin Xie, and Jian Yang. Ra-depth: Resolution adaptive self- supervised monocular depth estimation. In European Conference on Computer Vision, pp. 565- 581. Springer, 2022.
Marti A. Hearst. Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18-28, 1998.
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, and In So Kweon. Dpsnet: End-to-end deep plane sweep stereo. arXiv preprint arXiv: 1905.90500000 00538, 2019.
Prannay Khosla, Piotr Teterwak, Chen Wang. Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. NeurlPS, 33:18661- 18673, 2020.
Diederik Kingma and Jimmy Ba. Adam: method for stochastic optimization. arXiv preprint arXiv: 2014.
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit Cottereau, and Wei Tsang Ooi. Robodepth: Robust out-of- distiiiiiiittion depth estimation under corruptions. Advances in Neural Information Processing Systems, 36, 2024.
Balaji Lakshminarayanann Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017.
Jin Han Lee, Myung-Ky Kyu Han. Dong Wook Ko, and Il Hong Suh. From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv: /907. 10326, 2019.
Xiaoxiao Long, Lingjie Liu, Wei Li, Christian Theobalt, and Wenping Wang. Multi-view depth estimation using epipolar spatio-temporal networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8258-8288888 0066-826,,11 2021.
Xiaoyang Lyu, Liang Liu, Mengmeng Wang. Xin Kong, Lina Liu, Yong Liu, Xinxin Chen, and Yi Yuan. Hr-depth: High resolution self-supervised monocular depth estimation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp. 2294-2301 2021.
Matthew Matl,
https: httt::///ii.abb...ommmaaa///////
2019.
Zak Murez, Tarrence Van As, James Bartolozzi, Ayan Sinha, Vijay Badrinarayanan, and Andrew Rabinovich. Atlas; End-to-end 3d scene reconstruction from posed images. In Computer Vision- ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VII 16, pp. 431 Springer, 2020.
12

---

Published as a conference paper at ICLR 2025
Richard A Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J Davison, Pushmeet Kohi, Jamie Shotton, Steve Hodges, and Andrew Fitzgibbon. Kinectfusion: Real-time dense surface mapping and tracking. In 2011 10th IEEE international sympostum mixed and augmented reality, pp. 127-136. leee, 2011.
Matteo Poggi, Filippo Alcotti, Fabio Tosi, and Stefano Mattoccia. On the uncertainty of self- supervised monocular depth estimation. In Proceedings of the IEEE/CVF Conference Com- puter Vision and Pattern Recognition, pp. 3227-3237, 2020.
Pierluigi Zama Ramirez, Alex Costanzino, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoc- cia, and Luigi Di Stefano. Booster: a benchmark for depth from images of specular and transparent surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
Kieran Saunders, George Vogiatzis, and Luis Manso. Self-supervised monocular depth estima- tion: Let's talk about the weather. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 8907-8917, 2023.
Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 815-823, 2015.
Xuepeng Shi, Georgi Dikov, Gerhard Reitmayr, Tae-Kyun Kim, and Mohsen Ghafoorian. 3d distilla- tion: Improving self- superviee monocular depth estimation on reflective surfaces. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9133-9143, 2023.
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon. Scene coordinate regression forests for camera relocalization in rgb-d images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2930-2931 2013.
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and sup- port inference from rgbd images. In Computer Rision-ECCY 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V 12, pp- 746-760. Springer, 2012.
Adrian Spurr, Aneesh Dahiya, Xi Wang, Xucong Zhang, and Otmar Hilliges. Self-supervised 3d hand pose estimation from monocular rgb via contrastive learning. In ICCV, pp. 11230-11239, 2021.
Yaoming Wang. Yangzhou Jiang, Jin Li, Bingbing Ni, Wenrui Dai, Chenglin Li, Hongkai Xiong. and Teng Li. Contrastive regression for domain adaptation on gaze estimation. In CVPR, pp. 19376-19385. 2022.
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600- 612. 2004.
Kaiwen Zha, Peng Cao, Jeany Son, Yuzhe Yang, and Dina Katabi. Rank-n-contrast: Learning tinuous representations for regression. Advances in Neural Information Processing Systems, 36, 2024.
Ning Zhang, Francesco Nex, George Vosselman, and Norman Kerle. Lite-mono: A lightweight enn and transformer architecture for self-supervised monocular depth estimation. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 18537-18546, 2023.
Chaoqiang Zhao, Youmin Zhang, Matteo Poggi, Fabio Tosi, Xianda Guo, Zheng Zhu, Guan Huang, Yang Tang, and Stefano Mattoccia. Monovit: Self-supervised monocular depth estimation with a vision transformer. In 2022 International Conference 3D Vision (3DV), pp. 668-678. IEEE, 2022.
Chaoqiang Zhao, Matteo Poggi. Fabio Tosi, Lei Zhou, Qiyu Sun, Yang Tang, and Stefano Mattoccia. Gasmono: Geometry-aided self-supe monocular depth estimation for indoor scenes. In Proceedings of the iEEE/CVF International Conference on Computer Vision, pp. 16209-16220, 2023.
13

---

Published as a conference paper at ICLR 2025
Hang Zhou, David Greenwood, and Sarah Taylor. Self-supervised monocular depth estimation with internal feature fusion. arXiv preprint arXiv:2110.09482, 2021.
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe. Unsupervised learning of depth and ego-motion from video. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 1851-1858, 2017.
Xiaowei Zhou, Haoyu Guo, Sida Peng, Yuxi Xiao, Haotong Lin, Qianqian Wang. Guofeng Zhang, and Hujun Bao. Neural 3d scene reconstruction with indoor planar priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
A MORE DETAILED EXPERIMENTAL SETUPS
As aforementioned in the main manuscripts, we follow all training details and experimental setups mentioned in 3D Distillation (Shi et al. 2023). We train all models with the reflection triplet split proposed by 3D Distillation for 41 epochs through the Adam optimizer (Kingma & Ba 2014) with an image resolution of 384 228,, implemented in PyTorch. The training batch sizes of the Mon- odepth2 (Godard et al., 2019). HRDepth (Lyu et al. 2021), and MonoViT (Zhao et al., 2022) are {12, 12, 8), respectively. The initial learning rate is 10 and we adopt the multi-step learning rate scheduler that decays the learning rate by y 0.1 once the number of epochs reaches one of the milestones 36). Moreover, with 3D Distillation, the pose between cameras is ground truth dur- ing training, and the minimum and maximum depths used for training and evaluation are Im and 10m. In evaluation, we do not apply post-processing techniques such as averaging the estimates of both the flipped and original images or using median scaling.
Table 5: Main results on the ScanNet-Original Test and Validation sets.
ScanNet-OOiginal Test Set
Backbone
Training Schemn
Method
Abs
Sq Rel
log |
L.25 1
L.25
Ead-to-End
Self Supervipe
Oury
0.185
0.189
0.116
0.109
0.407
0.405
0.217
0.217
0.731
0.730
0.921
0.923
0.974
0.975
Menodepch2
Self- Teaching
0.184
0.109
0.392
0.210
0.742
0.925
0.976
Multi-Stage
3D Distillation
0,181
0,105
0.388
0.208
0,746
0.927
0.976
Oury!
0.175
0.098
0.385
0.206
0.746
0,930
0.979
End-to-End
Self- Supe-Supeviseed
0.184
0.111
0.399
0.212
0,739
0.925
0.976
0.213
HRDeethh
Self- MefTTechhing
0.178
0.102
0.381
0.204
0.752
0.931
0.979
Multi-Stage
3D Distillation
0.176
0.098
0.378
0.202
0.754
0,932
0.979
Ourz!
0.173
0.096
0.375
0.202
0.755
0.934
0.980
Ead-to-End
Self Supervised
0.154
0.082
0.343
0.182
0,801
0.948
0,984
Oury
0.155
0.081
0.345
0.185
0.795
0.945
0.984
MonoVTT VI
Self- Teaching
0.152
0.081
0.329
0.177
0.811
0.948
0.983
Multi-Stage
Distillation
0.075
0.174
0.949
0.985
Ours?
0.149
0.075
0.335
0.179
0.805
0.949
0.980
ScunNet-Original Val. Set
Backhone
Training Scherne
Method
Sq Rel
log J
8<
1
1.25" 1
1.25
End-to End
Self-Supervised
0.167
0.100
0.385
0.203
0.764
0.935
0.981
Oury
0.090
0,201
0.765
Menodepth2
Self Teaching
0.160
0.090
0.365
0.193
0.780
0.941
0.983
Multi- Multi-Stage
3D Distillaton
0.157
0.083
0.357
0.190
0.782
0.943
0.985
Our!!
0.153
0.080
0.358
0.190
0.783
0.944
0.985
Ead-to-End
Self-Supervised
Oury
0.166
0.100
0.089
0.381
0.200
0.197
0.771
0.937
0.941
0.982
0.984
HRDopth
Self- Self-Tesching
0.159
0.090
0.360
0.190
0.785
0.943
0.984
Multi-Stage
Distillation
0.080
0.349
0.186
0.945
Oury!
0.151
0.078
0.350
0.186
0.790
0.948
0.987
End-to Eind
Self Self-Supervised
0.138
0.077
0.331
0.171
0.831
0.955
0.986
Ours
0.137
0.069
0.328
0.172
0.826
0.958
0.989
Mono VIT
Self-Teachine
0.133
0.071
0.314
0.163
0.844
0.959
0.988
Multi- Multi-Stage
Distilation
0.157
Ours?
0.129
0.062
0.310
0.166
0.840
0.961
0.990
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Published as a conference paper at ICLR 2025
B EVALUATIONS ON SCANNET DATASET
To demonstrate the generalizability of our proposed method, we conduct the experiment on several ScanNet (Dai et al. 2017) splits denoted as Oriiinal [Test, Val.) sets and ScanNer- Test set following Shi et al. (202)) and Fu al. (2018); Bae et al. (022)). respectively. ScanNet- Original sets include both reflective and non-reflective surfaces, it is well-suited to evaluate the impact of reflective surfaces training comprehensively. In addition, the ScanNet-Robust test set was used to measure the generalization performance of Monocular Depth Estimation in the Robust Vision Challenge 2018 (Geiger et al., 2018), as it is small-scale but suitable for evaluating general- ization performance.
B.1 EVALUATION ON SCANNET-ORIGINAL SETS
Table summarizes the quantitative evaluation results of the ScanNet-Original sets. We achieve steady performance improvement across most metrics for all backbone models in the end-to-end training scheme, suggesting that the proposed method minimizes the influence of reflective sur- faces, which contributes to the general depth estimation performance improvement. Furthermore, Our multi-stage training scheme (i.e., Ours?) dramatically elevates performance across various depth estimation models. For Monodepth2, Ourst achieves remarkable average increase of 5.28% on the test set and 6.52% on the validation set across all metrics. HRDepth reaps substantial benefits, with improvements of 4.83% sistently gains, with enhancements of 2.28% on the test set and 5.59% on the validation set. When benchmarked against 3D Distillation (Shi et al. 2023), Ourst provides enhanced performance for Monodepth2, showing an average increase of 1.76% on the test set and 0.87% on the validation set. HRDepth also gains an average of 0.71% the test set and 0.69% on the validation set. However, for ViT, Ours! shows a slight decline, with decreases of 1.09% on the test set and 1.92% on the validation set compared to 3D Distillation.
the test set and 7.19% the validation set. Likewise, Mono ViT con-
Table 6: Main results the Net-Routt Test set.
ScunNet- -Rouust Test
Backhone
Method
Abs Rel
Sq Rel
RMSE
RMSE log 1
d 1.25
8 < 1,25
8 1,25
Sclf Sclf-Supervised
0.193
0.118
0.395
0.219
0.729
0,921
0.973
Monodepth2
Ours
0.186
0.107
0,388
0.216
0.729
0.926
0.976
Ours!
0.179
0.099
0.371
0.207
0.744
0.930
0.978
Sell-Supervised
0.190
0.112
0,387
0.216
0.729
0.924
0.976
HRDepth
Ours
0.188
0.107
0.384
0.215
0.731
0.926
0.976
Ours!
0.177
0.095
0.362
0.203
0.750
0.935
0,979
Self-Supervised
0.158
0.082
0.328
0.181
0.799
0,948
0,984
ViT
Ours
0.155
0.078
0.183
0.798
0.949
0.985
Ours!
0.150
0.073
0.319
0.178
0.806
0.952
0.986
B.2 EVALUATION ON SCANNET-ROBUSE TEST SET
Table& the quantitative evaluation results of the ScanNet-Robust test set. Due to the 3D- distillation baselines (Shi et al. 2023) did not release the source code, and the reported performance on this split not existing, we compare the models trained by our methods methods across three backbones, similar to previous experiments. As depicted in Table our pro- posed methods (i.e., Ours, Ourst) achieve significant performance gains for all evaluation metrics and all backbones, consistently. Specifically, for Monodepth2, Ours and Ours? demonstrate an av- erage performance improvement of 2.42% and 5.49%, respectively, across all metrics. Similarly, for HRDepth, Ours showed an average improvement of 1. 1.03%, and Ours! achieved a 5.55% increase in performance across all metrics. In the case of Mono ViT, Ours resulted in an average performance improvement of 0.87%, and Ours' achieved 3.13% improvement across all metries. The consis- tent improvements across all metrics for Monodepth2, HRDepth, and Mono VIT indicate that our methods effectively mitigate the risk of erroneous learning induced by reflective surfaces.
the self-supervised
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Table Evaluation results on the ScanNet-Reflecche Test for the combination of state-of-the-an self-supervised monocular depth framework and our approach.
Backbone
Method
Abs Rel
Sq Rel
RMSE
RMSE
1.25
0 1.25
6 1,25
Self-Supervised
0.161
0.124
0.477
0.203
0.779
0.947
0.981
RA-Depth
RA-Depth +-Crri
0.138
0.091
0,444
0.186
0.804
0,963
0.987
RAA-eppth +htri Crid
0.130
0.076
0.402
0.171
0.834
0,966
0,990
Self Supervised
0.156
0.123
0.462
0.198
0.810
0.949
0.980
GasMono Etri
0,425
GasMono +Liri Cred
0.127
0.072
0.386
0.164
0.843
0.968
0,993
Self Self-Sapervised
0.179
0.172
0,517
0.221
0.775
0.935
0.973
Lite
Lite mono Livs
0,113
0.462
0.775
0.947
Lite-mono -ores Cyrktt
0.148
0.101
0.433
0.190
0.788
0.960
0.984
C EXTENSIBILITY ANALYSIS OF THE PROPOSED METHOD
To demonstrate performance improvements not only across different architectures but also by ap- plying our method while preserving of-te-of-the-ar methodologies, we evaluate the three outper- forming SSMDE models, including RA-Depth (He et al.] 2022). GasMono (Zhao et al. (023), Lite-mono (Zang et al., 2023), which have own unique methods (e.g., iterative self-distillation in GasMono). We apply our reflection-aware triplet mining loss (denoted [Baseline] Lire and reflection-awee aware knowledge distillation method (denoted as [Baseline] Ceri + Erka) to each base- line. Table summarizes the results of the CeanNet-Reflectiin test split, comparing the performance of each method when incorporating the triplet loss and our distillation method. The results demon- strate that our method provides substantial performance gains even for recent SoTA methods, under- scoring its robustness to reflective surfaces.
Table 8: Evaluation results the ScanNet-Reflectin Validation and ScanNet- Valida- tion sets. w.r.t. reflective region mask M,.
ScanNet-Reflectio Validation Set
NconNet-NoRcfictiion Validation Set
Backbone
Method
Abs Rel
Sq Rel ,
RMSE
8 1.25
Abs Rel |
Sq Rel
RMSE
5 1.25
M,
0.206
0.227
0.584
0.961
0.169
0.100
0.395
0.979
Monodepth2
M, 0.170
0.132
0.505
0.979
0.171
0.099
0.402
0.978
Ours
0.166
0.125
0.492
0.981
0.168
0.095
0,395
0,980
M,
0,213
0.605
0.961
0.169
0,102
0,388
HRDepth
M,
0.184
0.167
0.564
0.965
0.179
0.113
0.433
0.968
Ours
0.167
0.127
0.496
0.982
0.167
0.096
0.389
0.979
M, 0.179
0.206
0.557
0.963
0.140
0.074
0.333
0.984
MinoVIT
M, 0.155
0.151
0.527
0,971
0.1G8
0.112
0.420
0.954
Ours 0.139
0.107
0.452
0.984
0.141
0.072
0.338
0.987
D IMPACT OF THE REFLECTIOON-WARE TRIPLET MINING LOSS W.R.T.
REFLECTIVE REGION LOCALIZATION
As aforementioned in the main manuscript, the proposed reflection-awart triplet mining loss is ap- plied to reflective regions, thus preserving performance non-reflective regions. To validate this claim, we conduct an experiment to evaluate the impact of varying the reflection mask M, with three configurations as follows:
1. M, 0: This configuration exactly corresponds to the traditional self-supervised method without the triplet mining loss.
2. M, = 1: In this configuration, the triplet loss is applied to both reflective and non-reflective regions of the image.
3. Ours: This configuration leverages M,, which is calculated through Equation X in the main manuscript, to selectively regulate the reflective regions.
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RGB
GT Depth
RGB
GT Depth
RGB
GT Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
M, -
M,
Ours
Figure 4: Qualitative results of the proposed methods w.r.t. reflective region mask My.
As shown in Table the results demonstrate that Ours significantly improves performance on reflec- tive datasets while maintaining comparable performance on non-reflective regions when compared the first configuration (denoted as M, 0). On the other hand, applying the triplet mining loss across all regions (M, 1) led to some performance improvement in reflective regions but resulted in notable drop performance in non-reflective regions compared to other configurations. These findings verify that the proposed reflection-aware triplet mining loss effectively identifies reflective regions and applies the triplet loss selectively, thereby preserving the performance non-reflective regions.
Table 9; Computational overhead comparison.
Method
Task
Training cost (hours)
Self-supervised (1)
End-to-end training
11.5
Ours (triplet loss) (2)
End-to-end training
14.1
Ours (distillation)
(3)
Multi-stage training
27.3
3D Distillation
Total (1+2+3+4)
95.5
1. Ensemble model pre-training
65.8
Ensemble model inference time
4.8
3. Mesh reconstruction and rendering
11.7
4. Student model training
13.2
E COMPUTATIONAL OVERHEAD COMPARISON WITH 3D DISTILLATION
The proposed triplet mining loss has negligible impact on the training cost of the model. Although the proposed simple distillation method involves multi-stage training and incurs additional training
costs, it remains significantly more efficient than the existing 3D distillation approach (Shi et al. 2023). To provide clarity, we summarized the training costs of Monodepth2 (Godard et al., 2019) under four scenarios: (1) training with traditional self self-supervision, (2) applying triplet loss, (3) applying our proposed distillation method, and (4) employing the 3D distillation approach. All train- ing times measured using single RTX A6000 GPU, as detailed in the Table
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RGB
GT Depth
RGB
GT Depth
RGB
GT Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
Self
Sup.
Ours
Ourst
(a) Qualitative results of proposed methods on the 7-Scenes dataset.
RGB
GT Depth
RGB
GT Depth
RGB
GT Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
Error Map
Pred. Depth
Self
Sup.
Ours
Ourst
High
kim
Lav
With
50.8
Riie
(b) Qualitative results of the proposed methods on the Booster dataset.
Figure 5: Qualitative results the proposed methods on the 7-scenes and Booster datasets. Note that the eтror map represents the absolute difference between prediction and ground truth depth, normalized to between and 255.
F QUALITATIVE RESULTS ON 7-SCENES AND BOOSTER DATASETS
We provide additional qualitative results of the proposed methods denoted as Ours and Ours! as discussed in Table of the main manuscript, utilizing the 7-Scenes (Shotton et al. 2013) and Booster (Ramirez et 2023 datasets. In Figure 5 showease the predicted depth and error map of the Monodepth2 trained by self-supervised. Ltvis and Lytd- Our methods alleviate the black-hole effect specular highlight regions while preserving high-frequency details non-reflective areas.
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As demonstrated by the qualitative evaluation of the 7-Scenes dataset, proposed methods exhibit robustness to reflective surfaces and impressive performance in preserving details on non-reflective surfaces in other indoor scenes that similar to the training environment.
G LIMITATIONS
Despite the promising results, study has several limitations. One major limitation is that the proposed method cannot handle transparent or mirror (ToM) objects. Secondly, a few cases do not satisfy the assumption of Equation 8 of the manuscript (e.g., surfaces including multiple reflection lobes). Lastly, similar to 3D distillation (Shi et al.) [023), the conducted experiments assume that the ground truth camera pose is known during training. Future research should aim address this limi- tation by exploring more effective solutions. We encourage future researchers to further investigate this issue and develop improved methodologies overcome these challenges.
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Reviews:
[{"review_id": "jcweoaBl0H", "reviewer": "Reviewer_ptjG", "summary": "The paper proposes a reflection-aware training method for self-supervised monocular depth estimation. The proposed method leverages the triplet mining loss to regularize the inappropriate regions for photometric loss.\nKey contributions of this work includes:\n- Introducing reflection-aware triplet mining loss and knowledge distillation for reflective regions.\n- first end-to-end method for self-supervised monocular depth estimation in reflective regions.\n- diverse experimetns on various indoor datasets.", "questions": "I would like to start by thanking the authors for their contribution to this field with their submission. Here are some questions:\n\nQ1: This relates to W1. \bAre there any reasons for not including experiments on outdoor scenes, such as the KITTI dataset? The proposed method is demonstrated on indoor datasets, where pose prediction networks might be challenging to train due to prevalent rotational camera movements. I am curious whether this method is adaptable when a pose network is involved or if it can be applied to depth estimation tasks in outdoor scenes.\n\nQ2: This relates to W2. I am concerned about whether it is possible to say that the predicted depth maps for reflective regions always have small disparities (instead of randomness errors). Is there any provements or previous works showing this is true?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "S1. This is the first end-to-end method for self-supervised monocular depth estimation in reflective regions. The proposed triplet loss is very simple and can be integrated into any framework for SSMDE.\n\nS2. Diverse Experiments: The paper shows diverse experiments with multiple datasets and cross validations.\n\nS3.  Writing and Presentation: The paper is well-written and easy to understand.", "weaknesses": "W1. Many self-supervised monocular depth estimation methods are evaluated in outdoor scenes like KITTI. However, this paper shows their results in only indoor scenes using Scannet, 7-Scenes and Booster datasets.\n\nW2. I could not find the proof of the authors' assumption that reflective regions have low disparities (instead of larger disparities).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FqfPLRePSk", "reviewer": "Reviewer_jMZ7", "summary": "This paper proposes a novel self-supervised monocular depth estimation training strategy, which uses triplet mining loss and reflection-aware knowledge distillation to pinpoint reflective regions at the pixel level, resulting in a robust depth estimation.", "questions": "I would appreciate it If the authors can address my concerns in Weaknesses.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The proposed triplet mining loss can be integrated into a general monocular depth estimation framework, enhancing the accuracy of reflective surfaces.\n\n2.The proposed reflection-aware knowledge distillation enhances the accuracy of reflective surfaces while preserving high-frequency details of non-reflective surfaces.\n\n3.Experiments on multiple datasets demonstrate that the proposed method achieves state-of-the-art performance.", "weaknesses": "1.Ablation study on the proposed pixel-level reflection regions Mr are necessary to further analyze its effectiveness in reflection-aware triplet mining loss and reflection-aware knowledge distillation.\n\n2.Will the proposed triplet mining strategy and reflection-aware knowledge distillation introduce additional computational overhead?\n\n3.The description in lines 246-247 is confusing. The relative pose [R|t]s2r is obtained by computing the inverse of [R|t]s2r?\n\n4. Depth completion is an extended task from depth estimation. Thus, it would be better if adding some depth completion methods [1-5] into the related work section.\n\n[1] Bilateral Propagation Network for Depth Completion, CVPR 2024\n\n[2] Tri-Perspective View Decomposition for Geometry-Aware Depth Completion, CVPR 2024\n\n[3] RigNet: Repetitive Image Guided Network for Depth Completion, ECCV 2022\n\n[4] Self-supervised Sparse-to-Dense: Self- supervised Depth Completion from LiDAR and Monocular Camera, ICRA 2019\n\n[5] Desnet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion, AAAI 2023", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5GqSzVn2eM", "reviewer": "Reviewer_tqa3", "summary": "This paper aims to address inaccurate depth estimation on the reflective surfaces in a scene. To this end, this paper proposes a training strategy for self-supervised depth estimation by leveraging triplet mining. The key idea is to penalize the inappropriate photometric error minimization on the localized reflective regions while preserving depth accuracy on non-reflective areas. In addition, this paper introduces a reflection-aware knowledge distillation method that enables a student model to selectively learn the pixel-level knowledge from reflective and non-reflective regions. Finally, the proposed framework is validated on various indoor datasets.", "questions": "1.\tVerify effectiveness on some recent SOTA frameworks, such as [1,2,3]\n2.\tTraining and validation on common self-supervised depth estimation datasets like KITTI, NYUv2.\n3.\tSee weaknesses for other issues.\n\n[1] RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation.\n[2] GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes.\n[3] Self-supervised Monocular Depth Estimation with Large Kernel Attention.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.\tThis paper is well-written and the proposed method is easy to comprehend.\n2.\tThis method is simple and general, which can be used for most self-supervised depth estimation methods.", "weaknesses": "1.Whether Eq. (8) can accurately reflect the reflective surface? Due to the ambiguity of photometric errors, the magnitude of photometric errors is not even a measure of the accuracy of depth [1], let alone reflective surfaces .\n2.The overall performance is very marginal. From the results on the test set in Table 4, we can find that, like most end-to-end self-supervised training solutions, all comparison methods have limited improvement or even worse after using the idea proposed by the author. \n3.Experimental verification is inadequate. The selected baselines, Monodepth2(2019), HRDepth(2021), MonoViT(2020) are the early works, not the SOTA framework. I am concerned about the effectiveness of the triplet mining loss when incorporated into recent works. Moreover, there is a lack of comparison on common depth estimation datasets, such as KITTI, NYUv2, etc., where reflective surfaces also exist.\n\n[1] Frequency-aware self-supervised monocular depth estimation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)