Abstract: Methods and systems for estimating a depth of one or more image frames for use in a head-mounted device (HMD) are provided. The method includes capturing, by the HMD, the one or more image frames, extracting, by the HMD, one or more features from each of the one or more image frames, predicting, by the HMD, a minimum number of depth sampling points for estimating the depth of each of the one or more image frames using the one or more extracted features, and estimating, by the HMD, the depth of each of the one or more image frames using the corresponding minimum number of depth sampling points.

Abstract: A method for rendering a reflection for a virtual object in an electronic device is provided. The method includes obtaining one or more red, green, and blue (RGB) frames and a depth-map associated with an augmented reality (AR) scene visualized in the electronic device for a user, determining one or more reflective surfaces in the AR scene based on the obtained RGB frames and the depth-map, identifying a type of the determined one or more reflective surfaces in the AR scene, generating a reflection map for the determined one or more reflective surfaces based on a placement of the virtual object, the identified type of the determined one or more reflective surfaces, and applying the generated reflection map on the determined one or more reflective surfaces to render the reflection for the virtual object in the electronic device.

Abstract: A method for dynamically transferring a texture on a 3D object is disclosed. The method includes receiving a pattern associated with the texture to be applied onto the 3D object; identifying, using a vertex identification-based neural network, a set of vertices of a mesh of the 3D object; generating, using a texture generation-based neural network, respective texturing parameters for each vertex in the set of vertices based on the pattern, wherein the respective texturing parameters comprise a color vector and a displacement vector; generating a textured 3D object by applying the texture on the 3D object using the respective texturing parameters; and rendering a 2D image of the textured 3D object based on a rasterization of the textured 3D object.

Abstract: A method and system for generating three-dimensional (3D) model augmented related objects from a scene are provided. The method includes creating one or more 3D objects and placing the 3D objects into the 3D scene. Embodiments herein disclose methods and systems for generating 3D augmented reality (AR) objects from a scene. The method may capture an object from the scene, perform a coarse semantic segmentation on the identified object, derive connected contour, generate intermediate contour from at least one outer and inner contours, and configure three-dimensional mesh and texture mapping to generate a three-dimensional model of the captured object.

Abstract: A method for determining depth information of objects in a scene, includes: generating RGB images having a first pixel density using an RGB camera; generating a depth image having a second pixel density using a depth camera, the second pixel density being lower than the first pixel density; generating an attention map using the RGB images with a high pixel density; merging the RGB images and the depth image to generate a merged image; determining an uncertainty score of each pixel of a plurality of pixels in the merged image; determining a threshold for the uncertainty score based on a level of uncertainty in the plurality of pixels; determining a depth value for each pixel of the plurality of pixels exceeding the threshold of the uncertainty score; applying the depth value of each pixel of the plurality of pixels in the merged image; and generating the scene with the depth information based on the depth value.

Abstract: A method and system for generating three-dimensional (3D) model augmented related objects from a scene are provided. The method includes creating one or more 3D objects and placing the 3D objects into the 3D scene. Embodiments herein disclose methods and systems for generating 3D augmented reality (AR) objects from a scene. The method may capture an object from the scene, perform a coarse semantic segmentation on the identified object, derive connected contour, generate intermediate contour from at least one outer and inner contours, and configure three-dimensional mesh and texture mapping to generate a three-dimensional model of the captured object.

Abstract: A method for generating a high-dynamic-range (HDR) image using an imaging device is provided. The method includes capturing a long-exposure (LE) image, a short-exposure (SE) image, and an auto-exposure (AE) image of a scene, estimating motion information in the SE image and the LE image based on a reference image which is determined based on an image statistics parameter, aligning the SE image and the LE image using the motion information, generating a pixel-weight coefficient for each of the SE image, the LE image, and the AE image, generating an overlapped region mask corresponding to an overlapped region in each of the SE image, the LE image and the LE image, determining a modified pixel-weight coefficient in the overlapped region mask and correcting a brightness difference, and generating an HDR image from the SE image, the LE image and the AE image using the modified at least one pixel-weight coefficient.

Abstract: A method of selecting capture configuration based on scene analysis and an image capturing device are provided. The method includes analyzing by a processor, a scene currently being captured by an image capturing device having a plurality of imaging sensors, identifying a current image capturing mode of the image capturing device, setting at least one capture parameter for the plurality of the imaging sensors, upon determining that the at least one capture parameter of the current image capturing mode has to be changed, and determining a timing sequence for triggering the plurality of imaging sensors to capture a plurality of image frames based on the set at least one capture parameter.

Abstract: A method for generating a high-dynamic-range (HDR) image using an imaging device is provided. The method includes capturing a long-exposure (LE) image, a short-exposure (SE) image, and an auto-exposure (AE) image of a scene, estimating motion information in the SE image and the LE image based on a reference image which is determined based on an image statistics parameter, aligning the SE image and the LE image using the motion information, generating a pixel-weight coefficient for each of the SE image, the LE image, and the AE image, generating an overlapped region mask corresponding to an overlapped region in each of the SE image, the LE image and the LE image, determining a modified pixel-weight coefficient in the overlapped region mask and correcting a brightness difference, and generating an HDR image from the SE image, the LE image and the AE image using the modified at least one pixel-weight coefficient.

Abstract: A method of selecting capture configuration based on scene analysis and an image capturing device are provided. The method includes analyzing by a processor, a scene currently being captured by an image capturing device having a plurality of imaging sensors, identifying a current image capturing mode of the image capturing device, setting at least one capture parameter for the plurality of the imaging sensors, upon determining that the at least one capture parameter of the current image capturing mode has to be changed, and determining a timing sequence for triggering the plurality of imaging sensors to capture a plurality of image frames based on the set at least one capture parameter.