3D computer vision is a field focused on enabling machines to interpret and understand three-dimensional structures from visual data. Unlike traditional 2D computer vision, which processes flat images or videos, 3D computer vision works with depth, spatial relationships, and volumetric representations. The goal is to reconstruct or analyze objects and environments in three dimensions, often using data from cameras, sensors, or algorithms that infer depth from 2D inputs. This allows applications like robotics, augmented reality, and autonomous systems to interact with the physical world more accurately.
Techniques in 3D computer vision vary depending on the input data and use case. For example, stereo vision uses two or more cameras to capture images from slightly different angles, mimicking human binocular vision to estimate depth through triangulation. LiDAR (Light Detection and Ranging) sensors emit laser pulses to measure distances and create precise 3D point clouds. RGB-D cameras, like Microsoft’s Kinect, combine color (RGB) and depth (D) data to provide real-time 3D mapping. Algorithms such as Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) reconstruct 3D scenes from 2D image sequences by tracking feature points across frames. Deep learning approaches, like convolutional neural networks (CNNs), are also used to predict depth maps from single images by training on datasets with paired 2D and 3D data.
Practical applications of 3D computer vision are widespread. In robotics, robots use 3D perception to navigate environments, manipulate objects, or avoid obstacles. Autonomous vehicles rely on 3D data to detect pedestrians, other cars, and road boundaries. In healthcare, 3D reconstructions of organs from MRI or CT scans aid in surgical planning. Augmented reality apps overlay virtual objects onto the real world by aligning them with 3D scene geometry. Challenges include handling occlusions, computational complexity, and sensor limitations—for instance, LiDAR struggles with reflective surfaces, while stereo vision requires sufficient texture for matching features. Despite these hurdles, advancements in hardware and algorithms continue to expand the capabilities of 3D computer vision systems.