Title: SEMANTICALLY-GUIDED REPRESENTATION LEARN-ING FOR SELF-SUPERVISED MONOCULAR DEPTH

Abstract: Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.

1. INTRODUCTION:

Accurate depth estimation is a key problem in computer vision and robotics, as it is instrumental for perception, navigation, and planning. Although perceiving depth typically requires dedicated sensors (e.g., stereo rigs, LiDAR), learning to predict depth from monocular imagery can provide useful cues for a wide array of tasks (Michels et al., 2005; Kendall et al., 2018; Manhardt et al., 2019 ; Lee et al., 2019) . Going beyond supervised learning from direct measurements (Eigen et al., 2014) , self-supervised methods exploit geometry as supervision (Guo et al., 2018; Pillai et al., 2019; Zou et al., 2018; Yang et al., 2017) , therefore having the potential to leverage large scale datasets of raw videos to outperform supervised methods (Guizilini et al., 2019) .

Although depth from a single image is an ill-posed inverse problem, monocular depth networks are able to make accurate predictions by learning representations connecting the appearance of scenes and objects with their geometry in Euclidean 3D space. Due to perspective, there is indeed an equivariance relationship between the visual appearance of an object in 2D and its depth, when conditioned on the object's category. For instance, a car 25 meters away appears smaller (on the image plane) than a car only 5 meters away but bigger than a truck 50 meters away. Current depth estimation methods either do not leverage this structure explicitly or rely on strong semantic supervision to jointly optimize geometric consistency and a semantic proxy task in a multi-task objective (Ochs et al., 2019; Chen et al., 2019) , thus departing from the self-supervised paradigm.

In this paper, we explore how we can leverage semantic information to improve monocular depth prediction in a self-supervised way. Our main contribution is a novel architecture that uses a fixed pre-trained semantic segmentation network to guide geometric representation learning in a selfsupervised monocular depth network. In contrast to standard convolutional layers, our architecture uses pixel-adaptive convolutions (Su et al., 2019) to learn semantic-dependent representations that can better capture the aforementioned equivariance property. Leveraging semantics may nonetheless introduce category-specific biases. Our second contribution is a two-stage training process where we automatically detect the presence of a common bias on dynamic objects (projections at infinity) and resample the training set to de-bias it. Our method improves upon the state of the art in selfsupervised monocular depth estimation on the standard KITTI benchmark (Geiger et al., 2013) , both on average over pixels, over classes, and for dynamic categories in particular. The introduction of semantic information to improve depth estimates has been explored in prior works, and can be broadly divided into two categories. The first one uses semantic (or instance) information to mask out or properly model dynamic portions of the image, which are not accounted for in the photometric loss calculation. Güney & Geiger (2015) leveraged object knowledge in a Markov Random Field (MRF) to resolve stereo ambiguities, while Bai et al. (2016) used a conjunction of instance-level segmentation and epipolar constraints to reduce uncertainty in optical flow estimation. Casser et al. ( 2019) used instance-level masks to estimate motion models for different objects in the environment, and account for their external motion in the resulting warped image. The second category attempts to learn both tasks in a single framework, and uses consistency losses to ensure that both are optimized simultaneously and regularize each other, so the information contained in one task can be transferred to improve the other. For instance, Ochs et al. (2019) estimated depth with an ordinal classification loss similar to the standard semantic classification loss, and used empirical weighting to combine them into a single loss for optimization. Similarly, Chen et al. (2019) used a unified conditional decoder that can generate either semantic or depth estimates, and both outputs are used to generate a series of losses also combined using empirical weighting to generate the final loss to be optimized.

Our approach focuses instead on representation learning, exploiting semantic features into the selfsupervised depth network by using a pretrained semantic segmentation network to guide the generation of depth features. This is done using pixel-adaptive convolutions, recently proposed in Su et al. (2019) and applied to tasks such as depth upsampling using RGB images for feature guidance. We show that different depth networks can be readily modified to leverage this semantic feature guidance, ranging from widely used ResNets (He et al., 2016) to the current state-of-the-art PackNet (Guizilini et al., 2019) , with a consistent gain in performance across these architectures.

3. SELF-SUPERVISED STRUCTURE-FROM-MOTION:

Our semantically-guided architecture is developed within a self-supervised monocular depth estimation setting, commonly known as structure-from-motion (SfM). Learning in a self-supervised structure-from-motion setting requires two networks: a monocular depth model f D : I → D, that outputs a depth prediction D = f D (I(p)) for every pixel p in the target image I; and a monocular ego-motion estimator f x : (I t , I S ) → x t→S , that predicts the 6 DoF transformations for all s ∈ S given by x t→s = ( R t 0 1 ) ∈ SE(3) between the target image I t and a set of temporal context source images I s ∈ I S . In all reported experiments we use I t-1 and I t+1 as source images.

3.1. THE SELF-SUPERVISED OBJECTIVE LOSS:

We train the depth and pose networks simultaneously, using the same protocols and losses as described in Guizilini et al. (2019) . Our self-supervised objective loss consists of an appearance matching term L p that is imposed between the synthesized Ît and original I t target images, and a depth regularization term L s that ensures edge-aware smoothing in the depth estimates Dt . The final objective loss is averaged per pixel, pyramid scale and image batch, and is defined as:

L(I t , Ît ) = L p (I t , Ît ) + λ 1 L s ( Dt )

(1) where λ 1 is a weighting coefficient between the photometric L p and depth smoothness L s loss terms. Following Godard et al. (2017) and Zhou et al. (2017) , the similarity between synthesized Ît and original I t target images is estimated using a Structural Similarity (SSIM) term (Wang et al., 2004) combined with an L1 loss term, inducing the following overall photometric loss:

EQUATION

In order to regularize the depth in low gradient regions, we incorporate an edge-aware term similar to Godard et al. (2017) . This loss is weighted for each of the pyramid levels, decaying by a factor of 2 on each downsampling, starting with a weight of 1 for the 0 th pyramid level. 2019), which proposes novel packing and unpacking blocks to respectively downsample and upsample feature maps during the encoding and decoding stages. This network was selected due to its state-of-the-art performance in the task of self-supervised monocular depth estimation, so we can analyze if our proposed architecture is capable of further improving the current state-of-the-art. However, there are no restrictions as to which models our proposed semantically-guided architecture can be applied to, and in Section 5.4 we study its application to different depth networks.

4. SEMANTICALLY-GUIDED GEOMETRIC REPRESENTATION LEARNING:

In this section, we describe our method to inject semantic information into a self-supervised depth network via its augmentation with semantic-aware convolutions. Our proposed architecture is depicted in Figure 2 and is composed of two networks: a primary one, responsible for the generation of depth predictions D = f D (I(p)); and a secondary one, capable of producing semantic predictions. Only the first network is optimized during self-supervised learning; the semantic network is initialized from pretrained weights and is not further optimized. This is in contrast to the common practice of supervised (ImageNet) pretraining of depth encoders (Godard et al., 2018; Casser et al., 2019; Zou et al., 2018) : here instead of fine-tuning from pre-trained weights, we preserve these secondary weights to guide the feature learning process of the primary depth network. Our approach also differs from learning without forgetting (Li & Hoiem, 2017 ) by leveraging fixed intermediate feature representations as a way to maintain consistent semantic guidance throughout training.

Figure 2 : Diagram of our proposed architecture for self-supervised monocular depth estimation with semantically-guided feature learning. The semantic network is fixed and initialized from pretrained weights, while the depth network is trained end-to-end in a self-supervised way, including pixel-adaptive convolutions (Guidance) on its decoder to learn semantic-dependent geometric features.

4.1. SEMANTICALLY-GUIDED DEPTH FEATURES:

We leverage the information from the pretrained semantic network in the depth network through the use of pixel-adaptive convolutions (Su et al., 2019) . They were recently proposed to address some limitations inherent to the standard convolution operation, namely its translation invariance making it content-agnostic. While this significantly reduces the number of parameters of the resulting network, this might also lead to sub-optimal solutions under certain conditions important for geometric representation learning. For example, spatially-shared filters globally average the loss gradients over the entire image, forcing the network to learn weights that cannot leverage location-specific information beyond their limited receptive fields. Content-agnostic filters are unable to distinguish between different pixels that are visually similar (i.e. dark areas due to shadows or black objects) or generalize to similar objects that are visually different (i.e. cars with varying colors). In this work, we use pixel-adaptive convolutions to produce semantic-aware depth features, where the fixed information encoded in the semantic network is used to disambiguate geometric representations for the generation of multi-level depth features.

As shown in Figure 2 , we extract multi-level feature maps from the semantic network. For each feature map, we apply a 3 × 3 and a 1 × 1 convolutional layer followed by Group Normalization (Wu & He, 2018) and ELU non-linearities (Clevert et al., 2016) . These processed semantic feature maps are then used as guidance on their respective pixel-adaptive convolutional layers, following the formulation proposed in Su et al. (2019) :

EQUATION

In the above equation, f ∈ R D are processed features from the semantic network that will serve to guide the pixel-adaptive convolutions from the depth network, p = (x, y) T are pixel coordinates, with [p i -p j ] denoting 2D spatial offsets between pixels, W k×k are convolutional weights with kernel size k, Ω i defines a k×k convolutional window around i, v is the input signal to be convolved, and b ∈ R 1 is a bias term. K is the kernel used to calculate the correlation between guiding features, here chosen to be the standard Gaussian kernel:

EQUATION

where Σ ij is the covariance matrix between features f i and f j , here chosen to be a diagonal matrix σ 2 •I D , with σ as an extra learnable parameter for each convolutional filter. These kernel evaluations can be seen as a secondary set of weights applied to the standard convolutional weights, changing their impact on the resulting depth features depending on the content stored in the guiding semantic features. For example, the information contained in depth features pertaining to the sky should not be used to generate depth features describing a pedestrian, and this behavior is now captured as a larger distance between their corresponding semantic features, which in turn produces smaller weights for that particular convolutional filter. Note that the standard convolution can be considered a special case of the pixel-adaptive convolution, where ∀ ij, K(f i , f j ) = 1. 

4.3. TWO-STAGE TRAINING:

One well-known limitation of the self-supervised photometric loss is its inability to model dynamic objects, due to a static world assumption that only accounts for camera ego-motion (Godard et al., 2018; Casser et al., 2019) . A resulting common failure mode is the infinite depth problem, which is caused by the presence of objects moving at the same speed as the camera. This typically causes distinct holes in the predicted depth maps, with arbitrarily large values where these objects should be. This severely hinders the applicability of such models in real-world applications, particularly for automated driving, where the ability to detect and properly model dynamic objects is crucial. Moreover, this limitation may be further accentuated in our proposed semantically-guided architecture, as the infinite depth problem occurs mostly on dynamic categories (i.e. cars and motorcycles) and the semantic-aware features may reinforce this bias.

We propose a simple and efficient two-stage training method to detect and remove this bias from the training set. In the first stage, we learn a standard depth network on all available training data. This network, exhibiting the infinite depth problem, is then used to resample the dataset by automatically filtering out sequences with infinite depth predictions that violate a basic geometric prior. We indeed find that depth predictions for pixels corresponding to the nearby ground plane are generally robust. This enables getting a coarse estimate of the ground plane using RANSAC and detecting the number of pixels whose predicted depth projects them significantly below the ground. If that number is above a threshold, then the corresponding image is subsequently ignored (we found a conservative threshold of 10 to work well in all our experiments, filtering out roughly 5% of the KITTI training dataset). During the second stage, we retrain the network on the subsampled dataset (from scratch to avoid the previous local optimum). As this subsampled dataset is de-biased, the network learns better depth estimates on dynamic objects. This process can be repeated, but we find that two stages are enough to remove any traces of infinite depth in our experiments, as shown in Figure 3 .

5. EXPERIMENTAL RESULTS:



5.1. DATASETS:

We use the standard KITTI benchmark (Geiger et Afterwards, the depth and pose networks are fine-tuned on KITTI for 30 epochs, with the same parameters and halving the learning rates after every 12 epochs. This fine-tuning stage includes the proposed architecture, where information from the fixed semantic network, pretrained separately, is used to directly guide the generation of depth features. There is no direct supervision at any stage during depth training, all semantic information is derived from the fixed secondary network.

When pretraining the semantic segmentation network, we use a ResNet-50 backbone with Imagenet (Deng et al., 2009) pretrained weights and optimize the network for 48k iterations on the CityScapes dataset with a learning rate of 0.01, momentum of 0.9, weight decay of 10 -4 , and a batch size of 1 per GPU. Random scaling between (0.7, 1.3), random horizontal flipping, and a crop size of 1000×2000 are used for data augmentation. We decay the learning rate by a factor of 10 at iterations 36k and 44k. Once training is complete, the semantic segmentation network is fixed and becomes the only source of semantic information when fine-tuning the depth and pose networks on KITTI.

5.3. DEPTH ESTIMATION PERFORMANCE:

Our depth estimation results are summarized in Table 1 , where we compare our proposed architecture with other published works. From these results we can see that the introduction of semanticallyguided geometric representation learning further improves upon the current state of the art in selfsupervised monocular depth estimation from Guizilini et al. (2019) , which served as our baseline.

Our approach also outperforms other methods that leverage semantic information by a substantial margin, even those using ground-truth KITTI semantic segmentation and depth labels during train- 4 , while other columns indicate pixel-average metrics.

ing (Ochs et al., 2019) . Furthermore, in Figure 5 we also present qualitative results showing the improvements in depth estimation generated by our proposed framework, compared to our baseline. Note how our semantically-guided architecture produces sharper boundaries and better object delineation, especially in structures further away or not clearly distinguishable in the input image.

5.4. ABLATIVE ANALYSIS:



5.4.1. DIFFERENT DEPTH NETWORKS:

To better evaluate our main contribution, we provide an ablative analysis showing how it generalizes to different depth networks. To this end, we consider two variations of the widely used ResNet architecture as the encoder for our depth network: ResNet-18 and ResNet-50 (the same pretrained semantic network was used in all experiments). Depth estimation results considering these variations are shown in Table 2 , where we can see that our proposed semantically-guided architecture is able to consistently improve the performance of different depth networks, for all considered metrics.

5.4.2. CLASS-SPECIFIC DEPTH PERFORMANCE:

To further showcase the benefits of our semantically-guided architecture, we also provide classspecific evaluation metrics, as shown in Figure 4 . As we do not have ground-truth semantic segmentation for these images, we use the prediction of the semantic network to bin pixels per predicted category, and evaluate only on those pixels. From these results we can see that our proposed architecture consistently improves depth performance for pixels across all predicted classes, especially those containing fine-grained structures and sharp boundaries, e.g. poles and traffic signs. We also measure the impact of our two-stage training process, which is expected to address the infinite depth problem in dynamic objects. Although we find the pixel-average difference in performance to not be significant (see Table 2 ), there is a significant improvement in class-average depth estimation, from 0.121 to 0.117 Abs-Rel. This is because the number of pixels affected by the infinite depth problem is vastly smaller than the total number of pixels. However, when considering class-average depth evaluation, the improvement over classes such as cars (0.200 to 0.177 Abs-Rel) and motorcycles (0.091 to 0.069) becomes statistically significant. This further exemplifies the importance of fine-grained metrics in depth evaluation, so these underlying behaviors can be properly observed and accounted for in the development of new techniques.

6. CONCLUSION:

This paper introduces a novel architecture for self-supervised monocular depth estimation that leverages semantic information from a fixed pretrained network to guide the generation of multi-level depth features via pixel-adaptive convolutions. Our monodepth network learns semantic-aware geometric representations that can disambiguate photometric ambiguities in a self-supervised learning structure-from-motion context. Furthermore, we introduce a two-stage training process that resamples training data to overcome a common bias on dynamic objects resulting in predicting them at infinite depths. Our experiments on challenging real-world data shows that our proposed architecture consistently improves the performance of different monodepth architectures, thus establishing a new state of the art in self-supervised monocular depth estimation. Future directions of research include leveraging other sources of guidance (i.e. instance masks, optical flow, surface normals), as well as avenues for self-supervised fine-tuning of the semantic network.

A PRE-TRAINING THE SEMANTIC SEGMENTATION NETWORK:

The introduction of a semantic segmentation network to the depth estimation task increases the depth estimation performance, however it also increases model complexity (e.g. number of trainable parameters). To investigate that the increased performance for the depth estimation task is indeed due to the semantic features encoded in the secondary network, we perform an in-depth analysis (summarized in Table 3 ) where we explore the impact of pre-training the semantic segmentation network before it is used to guide the generation of depth features. From these results we can see that the presence of semantic information encoded in the secondary network indeed leads to an increase in performance, and that fine-tuning this secondary network for the speficic task of depth estimation actually decreases performance.

In the first two rows an untrained semantic network is utilized, with only its encoder initialized from ImageNet (Deng et al., 2009) weights. Two different scenarios are explored: in the first one (D) only the depth network is fine-tuned in a self-supervised fashion, while in D+S both networks are fine-tuned together in the same way. As expected, using untrained features as guidance leads to significantly worse results, since there is no structure encoded in the secondary network and the primary network needs to learn to filter out all this spurious information. When both networks are fine-tuned simultaneously, results improve because now the added complexity from the secondary network can be leveraged for the task of depth estimation, however there is still no improvement over the baseline.

Next, the semantic network was pre-trained on only half of the CityScapes (Cordts et al., 2016) dataset (samples chosen randomly), leading to a worse semantic segmentation performance (validation mIoU of around 70% vs. 75% for the fully trained one). This partial pre-training stage was enough to enable the transfer of useful information between networks, leading to improvements over the baseline. Interestingly, fine-tuning both networks for the task of depth estimation actually hurt performance this time, which we attribute to forgetting the information contained in the secondary network, as both networks are optimized for the depth task. When the semantic network is pretrained with all of CityScapes (last two rows), these effects are magnified, with fine-tuning only the depth network leading to our best reported performance (Table 1 ) and fine-tuning both networks again leading to results similar to the baseline. ). In the Fine-Tune column, D indicates fine-tuning the depth network and S indicates finetuning the semantic network (note that this is a self-supervised fine-tuning for the depth task, using the objective described in Section 3.1).

B UNCERTAINTY AND GENERALIZATION TO DIFFERENT OBJECTS:

In a self-supervised setting, increasing the number of unlabeled videos used for depth training is expected to lead to an increasing specialization away from the domain in which the semantic network was pre-trained. This might result in harmful guidance if our method is not robust to this gap. However, our approach does not use semantic predictions directly, but rather the decoded features of the semantic network themselves, which represent general appearance information that should be more robust to this domain gap. To validate our hypothesis, we further explore the impact of erroneous semantic information in the performance of our proposed semantically-guided depth framework. In Figure 6 we present qualitative results highlighting situations in which our pretrained semantic network failed to generate correct semantic predictions for certain objects in the scene, and yet our proposed framework was still able to properly recover depth values for that portion of the environment. These exemplify possible scenarios for erroneous semantic prediction.

• Imprecise boundaries: in the first row, we can see that the semantic segmentation network does not correctly detect the traffic sign, yet the semantically-guided depth network predicts its shape and depth accurately. • Wrong classification: in the second row, the truck was mistakenly classified as partially "road" and "building", however our semantically-guided depth network was still able to properly recover its overall shape with sharp delineation that was not available from its semantic contour. A similar scenario happens in the same image, with "fence" being partially labeled as "bicycle". • Missing ontology: there is no "trash can" class on the CityScapes ontology, however in the third row our semantically-guided depth network was able to correctly reconstruct such object even though it was classified as "fence", similarly to its surroundings. • Object Hallucination: in the fourth row, the contour of a "person" was erroneously introduced in the image and correctly removed by our semantically-guided framework.

These examples are evidence that our proposed framework is able to reason over the uncertainty inherent to semantic classification, leveraging this information when accurate to achieve the results reported in this paper, but also discarding it if necessary to generate a better reconstruction according to the self-supervised photometric loss. 

C GENERALIZATION TO DIFFERENT DATASETS:

In the previous sections, we show that our proposed framework is robust to a degraded semantic network, both by pretraining the semantic network with fewer annotated labels (Appendix A) and also by providing evidence that the depth network is able to reason over erroneous predictions to still generate accurate reconstructions (Appendix B). We now go one step further and analyze how our proposed semantically-guided framework generalizes to a dataset that was used neither during pre-training nor for fine-tuning. To this end, we evaluate our KITTI depth model on the recently released NuScenes dataset (Caesar et al., 2019) . The official NuScenes validation split is used, containing 6019 images from the front camera with ground-truth depth maps generated by LiDAR reprojection. Results presented in Table 4 provide additional evidence that our method indeed results in generalization improvements, even on significantly different data from different platforms

:



Figures and Tables:

Figure 1: Example of a pointcloud generated using our proposed semantically-guided architecture, colored by RGB values from the input image and corresponding predicted semantic labels.

s ( Dt ) = |δ x Dt |e -|δxIt| + |δ y Dt |e -|δyIt| (3) We also incorporate some of the insights introduced in Godard et al. (2018), namely auto-masking, minimum reprojection error, and inverse depth map upsampling to further improve depth estimation performance in our self-supervised monocular setting. 3.2 DEPTH AND POSE NETWORKS Our baseline depth and pose networks are based on the PackNet architecture introduced by Guizilini et al. (

Figure 3: Qualitative results of our proposed two-stage training to address the infinite depth problem. Top images were obtained evaluating the first-stage depth network, and bottom images were obtained using the second-stage depth network, trained with a filtered dataset.

IMPLEMENTATION DETAILS We implement our models with PyTorch (Paszke et al., 2017) and follow the same training protocols of Guizilini et al. (2019) when optimizing our depth and pose networks. The initial training stage is conducted on the CityScapes dataset for 50 epochs, with a batch size of 4 per GPU and initial depth and pose learning rates of 2 • 10 -4 and 5 • 10 -4 respectively, that are halved every 20 epochs.

Figure 4: Class-specific depth evaluation for our proposed architecture (blue), relative to our baseline (red). The rightmost column indicates class-average depth metrics, obtained by averaging all individual classes. The introduction of semantically-guided features, in conjunction with our proposed two-stage training methodology to address the infinite depth problem, consistently improved depth results for all considered classes (lower is better).

Figure 5: Qualitative results of our proposed architecture. The left, middle, and right columns show respectively input images, baseline predicted depth maps (Guizilini et al., 2019), and the depths maps obtained using our proposed architecture. Our semantic-aware depth network predicts sharper boundaries and fine-grained details on distant objects. The dotted lines indicate class-average errors, obtained by averaging all the class-specific depth errors.

Figure 6: Examples of erroneous semantic predictions that still led to accurate depth predictions using our proposed semantically-guided depth framework.

Rel RMSE RMSE log δ < 1.25 δ < 1.25 2 δ < 1.253   Quantitative performance comparison of our proposed architecture on KITTI for depths up to 80m. M refers to methods that train using monocular images, S refers to methods that train using stereo pairs, D refers to methods that use ground-truth depth supervision, Sem refers to methods that include semantic information, and Inst refers to methods that include semantic and instance information. MR indicates 640 x 192 input images, and HR indicates 1280 x 384 input images. Our proposed architecture is able to further improve the current state of the art in self-supervised monocular depth estimation, and outperforms other methods that exploit semantic information (including ground truth labels) by a substantial margin.

Table content: <table><tr><td colspan="3">Method Abs Rel Sq Garg et al. (2016) Lower is Better Superv. M 0.152 1.226 5.849</td><td>0.246</td><td>0.784</td><td>Higher is Better 0.921</td><td>0.967</td></tr><tr><td>Zou et al. (2018)</td><td>M</td><td>0.150 1.124 5.507</td><td>0.223</td><td>0.806</td><td>0.933</td><td>0.973</td></tr><tr><td>Godard et al. (2017)</td><td>M</td><td>0.141 1.186 5.677</td><td>0.238</td><td>0.809</td><td>0.928</td><td>0.969</td></tr><tr><td>Zhan et al. (2018)</td><td>M</td><td>0.135 1.132 5.585</td><td>0.229</td><td>0.820</td><td>0.933</td><td>0.971</td></tr><tr><td>Godard et al. (2018) (R18)</td><td>M</td><td>0.115 0.903 4.863</td><td>0.193</td><td>0.877</td><td>0.959</td><td>0.981</td></tr><tr><td>Godard et al. (2018) (R50)</td><td>M</td><td>0.112 0.851 4.754</td><td>0.190</td><td>0.881</td><td>0.960</td><td>0.981</td></tr><tr><td>Guizilini et al. (2019) (MR)</td><td>M</td><td>0.108 0.727 4.426</td><td>0.184</td><td>0.885</td><td>0.963</td><td>0.983</td></tr><tr><td>Guizilini et al. (2019) (HR)</td><td>M</td><td>0.104 0.758 4.386</td><td>0.182</td><td>0.895</td><td>0.964</td><td>0.982</td></tr><tr><td>Casser et al. (2019)</td><td colspan="2">S+Inst 0.141 1.025 5.290</td><td>0.215</td><td>0.816</td><td>0.945</td><td>0.979</td></tr><tr><td>Chen et al. (2019)</td><td colspan="2">S+Sem 0.118 0.905 5.096</td><td>0.211</td><td>0.839</td><td>0.945</td><td>0.977</td></tr><tr><td>Ochs et al. (2019)</td><td colspan="2">D+Sem 0.116 0.945 4.916</td><td>0.208</td><td>0.861</td><td>0.952</td><td>0.968</td></tr><tr><td>Ours (MR)</td><td colspan="2">M+Sem 0.102 0.698 4.381</td><td>0.178</td><td>0.896</td><td>0.964</td><td>0.984</td></tr><tr><td>Ours (HR)</td><td colspan="2">M+Sem 0.100 0.761 4.270</td><td>0.175</td><td>0.902</td><td>0.965</td><td>0.982</td></tr></table>

Abs Rel Sq Rel RMSE RMSE log δ < 1.25 δ < 1.25 2 δ < 1.25 3 Abs Rel Ablative analysis of our semantic guidance (SEM) and two-stage-training (TST) contributions. The last column indicates class-average Abs. Rel. obtained by averaging all class-specific depth errors in Figure

Table content: <table><tr><td>Network SEM TST</td><td>Lower is Better</td><td/><td/><td>Higher is Better</td><td/><td>Class-Avg.</td></tr><tr><td>ResNet-18</td><td>0.120 0.896 4.869 0.117 0.854 4.714</td><td>0.198 0.191</td><td>0.868 0.873</td><td>0.957 0.963</td><td>0.981 0.981</td><td>0.149 0.139</td></tr><tr><td>ResNet-50</td><td>0.117 0.900 4.826 0.113 0.831 4.663</td><td>0.196 0.189</td><td>0.873 0.878</td><td>0.967 0.971</td><td>0.980 0.983</td><td>0.144 0.136</td></tr><tr><td/><td>0.108 0.727 4.426</td><td>0.184</td><td>0.885</td><td>0.963</td><td>0.983</td><td>0.132</td></tr><tr><td>PackNet</td><td>0.103 0.710 4.301</td><td>0.179</td><td>0.895</td><td>0.964</td><td>0.984</td><td>0.121</td></tr><tr><td/><td>0.102 0.698 4.381</td><td>0.178</td><td>0.896</td><td>0.963</td><td>0.984</td><td>0.117</td></tr></table>

Analysis of the impact of pre-training the semantic segmentation network. On the Pre-Train column, I indicates ImageNet (Deng et al., 2009) pretraining and CS indicates CityScapes (Cordts et al., 2016) pretraining, with 1/2 indicating the use of only half the dataset (samples chosen randomly

Table content: <table/>

Peer review (iclr2020):
The paper proposes a using pixel-adaptive convolutions to leverage semantic labels in self-supervised monocular depth estimation. The semantic features are predicted by a pretrained network rather than relying on a ground truth. Moreover, a two-stage training process in proposed in order to filter out images leading to erroneous SfM predictions. The method is evaluated with different networks on the KITTY dataset. The paper is very well written and clear. The applications of per-pixel convolutions to this problem seems sound and the experimental validation seems satisfactory. I have however one main concern (1) and a few additional questions below: 1) While (Guizilini 2019) shows that using a larger set of unannotated videos and allows the self-supervised method to eventually outperform supervised methods, this study is not done here. This makes me question the applicability of the approach, as using large unlabelled videos would probably lead to noisy segmentations that could be unhelpful to the depth estimation. Showing an improvement over the supervised baseline would be a much stronger experimental validation, as for now it is difficult to know exactly why in which scenario this method should be used, rather than a supervised network or vanilla packnet. 2) I see that you obtain the same numbers in Table 2 / PackNet / row 1 as in (Guizilini 2019); I would like to confirm that you used exactly their self-objective loss, in all your experiments? I would suggest adding to section 3.1. the fact the fact that the loss is the same is in (Guizlini 2019), as a reader could assume that there is novelty in the loss formulation. 3) Have you tried fine-tuning the whole architecture including the semantic network end-to-end?