# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 4 Experiments
### Experimental Setup
#### 4.1.2 Implementation Details  
We process RGB inputs from six cameras: \(CAM\_FRONT\_LEFT\), \(CAM\_FRONT\), \(CAM\_FRONT\_RIGHT\), \(CAM\_BACK\_LEFT\), \(CAM\_BACK\), and \(CAM\_BACK\_RIGHT\). The images in the dataset have an original resolution of \(900\times 1600\). These imagesare normalized using a mean of \([123.675,116.28,103.53]\) and a standard deviation of \([58.395,57.12,57.375]\).  
To augment the image data, we apply transformations consistent with prior works [2; 27], including random cropping, random scaling, random flipping, and random rotation. Random resizing is applied within a range of \((0.386,0.55)\), random rotation is constrained to \((-5.4^{\circ},5.4^{\circ})\), and random flipping is performed. After augmentation, the resulting image size becomes \(256\times 704\). In addition to image-level augmentations, we also employ BEV (Bird's Eye View) data augmentations, which include random scaling, random flipping, and random rotation. Rotation is applied within \((-22.5^{\circ},22.5^{\circ})\), scaling ranges between \((0.95,1.05)\), and flipping occurs with a probability of 0.5 along both the \(x\)- and \(y\)-axes. The BEV data are processed in a spatial grid of \(128\times 128\). The chosen augmentation parameters aim to simulate realistic variations, such as minor misalignments in camera orientation or slight tilts due to road inclines. For example, the image rotation range of \(\pm 5.4^{\circ}\) is subtle enough to reflect these realistic scenarios without introducing unrealistic distortions.  
For the monocular pipeline, we use the DLA-34 backbone [48], consistent with prior works [1; 49; 50]. This backbone processes augmented images with a resolution of \(256\times 704\). The detector head consists of multiple prediction branches, each responsible for regressing different object properties along with their associated uncertainties. Each branch follows a two-layer structure: a \(3\times 3\times 256\) convolutional feature extraction layer with batch normalization and ReLU activation, followed by a \(1\times 1\times c_{o}\) convolutional prediction layer, where \(c_{o}\) denotes the output channel dimension specific to each task. As in [1], we incorporate an edge fusion mechanism to enhance feature learning. This mechanism improves the network's ability to capture object boundaries and structural relationships in the image. Additionally, we adopt an adaptive depth ensemble of \(M+1\), combining direct regression with \(M\) geometric solutions derived from keypoints, weighted by their uncertainties.  
For the BEV pipeline, we use ResNet-50 [51] pretrained on ImageNet (via torchvision) as the image backbone to process augmented images with a resolution of \(256\times 704\). To extract BEV features, we employ ResNet-18 [51]. Two-dimensional context features are transformed into a unified 3D space, specifically a frustum of dimensions \(112\times 16\times 44\), representing each camera's field of view. Using the voxel pooling mechanism proposed by [2], we construct BEV data with dimensions of \(128\times 128\).