I'm working on 3d meshes dataset, i have to label it to train my deep learning model for a segmentation task like the picture shows. I spent days looking for a tool to label my 3d data but unfortunately i found nothing.. What tool i can use to label my data (3d mesh, .obj files) for segmentation task?
I am working on creating a model that locates an object in the scene (2D image or 3D scene) using a natural language query. I came across this paper on natural language object retrieval that mentions that this task is different from text-based image retrieval in the sense that natural language object retrieval requires an understanding of objects in the image, spatial configurations, etc. I am not able to see the difference between these two tasks. Could you please explain …
The exact problem is a crate of industrial parts, made by injection molding in very high quantities. The objective is to put as much parts as possible in one crate. This is done by a small robotic arm that take the part from the injection molding machine, cold it a little and put it in the crate. The shape of the parts can be a bit complex. It can be basically anything that can be molded into a 2 parts …
I am working on 3D object detection for parcel boxes same like amazon parcel,thinking to label with 3D cuboids but finding difficult to find a existing 3D object detection for 2D images. As far what i have found is kitti & MMDetection only which requires point cloud data which means for those images coming from LIDAR or RADAR. Is there any other method for object detection & do find the reference image.
My goal is to detect whether a MRI image contains an anomaly and the location of the anomaly. In my dataset I have MRI brain images which contain values of electrical conductivity of brain tissues. Tissue segmentation image slice (left) and MRI conductivity image slice (right): Anomaly has bigger conductivity as white matter, so I have to detect an anomaly based on that property. On tissue segmentation image, anomaly is presented with yellow color. I have segmented white matter from …
I have a ground truth bounding box for a 3d object. I would like to extract useful features for the object. My goal is to concatenate these visual object features with language features (from the description of the object) for training. For visual features, I want to use the ground-truth bounding boxes to create an upper baseline. How do I extract features using 3d bounding box?
I don't at all understand the format for the Kitti 3D Object Detection Evaluation 2017 dataset ground truth labels. Since there are many Kitti datasets, let me be clear that I'm talking about the set available at http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d, then downloading the Velodyne point clouds and training labels: Every source I can find, for example https://github.com/NVIDIA/DIGITS/blob/master/digits/extensions/data/objectDetection/README.md or https://medium.com/@desjoerdhaan/kitti-3d-object-detection-data-set-ef8ee6409574, stated that the columns are as follows: type truncated occluded alpha x1 y1 x2 y2 height width length x y z yaw where …
I am trying to do Segmentation on my custom dataset consisting of 3D CAD Models. However, I am not able to find the proper tools to generate my dataset for Segmentation. I am able to find the existing dataset with segmentation information, but am not able to find any information on how to create one for our own CAD Models. I need to train the model on my custom dataset hence creating one. I will really appreciate any help that …