Intel's RealSense depth cameras have had major applications in the field of robotics, giving machines 3D vision and helping robots navigate without the need for GPS. Today, the company has unveiled a new model -- the D455 -- which comes with a longer range and increased precision that's twice that of previous generations. The new camera's optimal range is six meters, making it twice as accurate as the current D400, without sacrificing its field of view. It also comes with global shutters for depth and RGB sensors that not only improve communication between the different data streams picked up by the stereo camera, but increases color clarity overall. Because of these color-focused improvements, the camera works better in a variety of lighting conditions. According to Intel, the D455 has been designed to give developers greater freedom in developing vision-based products that need to understand, interact and learn from their environments.
Eyewear maker Warby Parker has updated its Glasses app for iOS to include an iPhone X-only recommendation feature. Let the app scan your face and it'll recommend the frames that are most likely to fit your measurements. This isn't the same as modeling the frames on your face (wouldn't the iPhone X be ideal for that?), but it could save you a lot of hemming and hawing as you wonder which styles are a good match. This is something of a niche use -- how often do you go shopping for frames, really? With that said, it illustrates how a depth-aware front cam can serve a genuinely practical purpose.
The human brain possesses the remarkable ability to infer depth when viewing a two-dimensional scene, even with a single-point measurement, as in viewing a photograph. However, accurate depth mapping from a single image remains a challenge in computer vision. Depth information from a scene is valuable for many tasks like augmented reality, robotics, self driving cars etc. In this blog we explore how to train a depth estimation model on NYU Depth Data set. The model gets state of the art results on this data set.
Abstract: We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal. Similarly to prior work, our method learns by applying differentiable warping to frames and comparing the result to adjacent ones, but it provides several improvements: We address occlusions geometrically and differentiably, directly using the depth maps as predicted during training. We introduce randomized layer normalization, a novel powerful regularizer, and we account for object motion relative to the scene. To the best of our knowledge, our work is the first to learn the camera intrinsic parameters, including lens distortion, from video in an unsupervised manner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale. We evaluate our results on the Cityscapes, KITTI and EuRoC datasets, establishing new state of the art on depth prediction and odometry, and demonstrate qualitatively that depth prediction can be learned from a collection of YouTube videos.
Rumors of what the next iPhone will be like are coming in hot and heavy. Last week, well-connected Apple analyst Ming-Chi Kuo noted that the new handsets would nix the home button for a touch-friendly "function area." In a KGI Securities report detailed by 9to5Mac, the analyst explains that the upcoming OLED iPhone will feature a "revolutionary" front camera that's capable of sensing 3D space via infrared. More specifically, the report explains that the newfangled camera can combine depth information with 2D images for things like facial recognition, iris recognition and, perhaps most importantly, 3D selfies. Given the previous report about the home button being put out to pasture, there will need to be a replacement for Touch ID.