👁️Computer Vision and Image Processing Unit 8 – 3D Vision and Depth Perception

Key Concepts in 3D Vision

• 3D vision aims to extract 3D information from 2D images enables computers to perceive depth and structure in the environment
• Involves understanding the geometry of the scene, the relative positions and orientations of objects, and their 3D shapes and sizes
• Relies on the principles of projective geometry describes how 3D points are mapped onto 2D image planes
• Utilizes multiple views of the same scene (stereo vision) or motion information (structure from motion) to infer depth
• Requires calibration of camera parameters (intrinsic and extrinsic) to establish the relationship between 3D world coordinates and 2D image coordinates
• Deals with challenges such as occlusions, textureless regions, and varying lighting conditions that can affect the accuracy of 3D reconstruction
• Finds applications in robotics (navigation and manipulation), augmented reality, autonomous vehicles, and 3D modeling and animation

Depth Cues and Stereopsis

• Depth cues are visual information that helps humans perceive depth and 3D structure in the environment
• Monocular cues rely on a single eye and include linear perspective (parallel lines converging), relative size (closer objects appear larger), occlusion (closer objects occlude farther objects), and atmospheric perspective (distant objects appear hazier)
• Binocular cues require both eyes and are based on the slight differences in the images seen by each eye (binocular disparity)
• Stereopsis is the process of perceiving depth from binocular disparity allows the brain to fuse the two slightly different images into a single 3D perception
• The distance between the eyes (interpupillary distance) and the convergence angle of the eyes provide additional depth information
• Motion parallax is another depth cue that relies on the relative motion of objects as the observer moves closer objects appear to move faster than farther objects
• Accommodation (focusing of the eye's lens) and convergence (inward rotation of the eyes) also provide depth information, but are limited to short distances

Camera Models and Calibration

• Camera models describe the mathematical relationship between 3D world coordinates and 2D image coordinates
• The pinhole camera model is a simple and widely used model assumes light rays pass through a single point (the camera center) and form an inverted image on the image plane
• The pinhole model is characterized by intrinsic parameters (focal length, principal point, and skew) and extrinsic parameters (rotation and translation of the camera relative to the world coordinate system)
• Lens distortion (radial and tangential) is an additional factor that affects the mapping between 3D and 2D coordinates and needs to be accounted for in real cameras
• Camera calibration is the process of estimating the intrinsic and extrinsic parameters of a camera
  • Intrinsic calibration involves estimating the focal length, principal point, and distortion coefficients using known 3D-2D correspondences (e.g., checkerboard patterns)
  • Extrinsic calibration involves estimating the rotation and translation of the camera relative to a known world coordinate system
• Accurate camera calibration is crucial for 3D reconstruction and measurement tasks

Stereo Vision Techniques

• Stereo vision involves using two or more cameras to capture different views of the same scene and extract depth information
• The main steps in stereo vision are camera calibration, image rectification, stereo matching, and depth estimation
• Image rectification is the process of transforming the images so that corresponding points lie on the same horizontal line (epipolar line) simplifies the stereo matching problem
• Stereo matching involves finding corresponding points in the left and right images that represent the same 3D point in the scene
  • Block matching is a simple and efficient stereo matching technique that compares small patches (blocks) of pixels between the images
  • Global optimization methods (e.g., graph cuts, belief propagation) aim to find the best disparity assignment by minimizing a global energy function
• Disparity is the horizontal shift between corresponding points in the left and right images and is inversely proportional to depth
• Depth can be estimated from the disparity using the camera parameters and the known baseline (distance) between the cameras
• Challenges in stereo vision include occlusions, textureless regions, and repetitive patterns that can lead to ambiguities in stereo matching

Structure from Motion

• Structure from motion (SfM) is a technique for estimating the 3D structure of a scene and the camera motion from a sequence of 2D images
• SfM relies on the principle that the 2D motion of points in the images is related to the 3D structure of the scene and the motion of the camera
• The main steps in SfM are feature detection and matching, camera pose estimation, triangulation, and bundle adjustment
• Feature detection involves identifying distinctive points (features) in the images that can be reliably matched across different views
  • Common feature detectors include SIFT, SURF, and ORB, which are invariant to scale, rotation, and illumination changes
• Feature matching involves finding corresponding features across different images using similarity measures (e.g., Euclidean distance, correlation)
• Camera pose estimation involves determining the position and orientation of the camera for each image in the sequence using the matched features and the camera model
• Triangulation is the process of estimating the 3D coordinates of the matched features using the estimated camera poses and the camera model
• Bundle adjustment is a global optimization step that refines the estimated camera poses and 3D points by minimizing the reprojection error (difference between the observed and predicted image points)
• SfM can handle uncalibrated cameras and does not require known 3D-2D correspondences, making it more flexible than traditional stereo vision techniques

3D Reconstruction Methods

• 3D reconstruction is the process of creating a 3D model of an object or scene from multiple 2D images or depth measurements
• Passive methods rely on images captured under natural illumination and include stereo vision, structure from motion, and multi-view stereo
  • Multi-view stereo (MVS) extends the concepts of stereo vision to multiple images and aims to estimate a dense 3D point cloud or surface model
• Active methods use controlled illumination or sensing techniques to directly measure depth or 3D structure
  • Structured light involves projecting known patterns (e.g., stripes, dots) onto the scene and inferring depth from the deformation of the patterns
  • Time-of-flight (ToF) cameras measure the time it takes for light to travel from the camera to the scene and back, providing a direct depth measurement
• Volumetric methods (e.g., voxel coloring, space carving) represent the 3D space as a grid of voxels (3D pixels) and carve out the empty space based on the consistency of the images
• Surface-based methods (e.g., Poisson surface reconstruction) aim to reconstruct a continuous surface model from the 3D point cloud
• Texture mapping is the process of projecting the color information from the images onto the reconstructed 3D model to enhance its visual appearance
• Challenges in 3D reconstruction include dealing with occlusions, textureless regions, and complex or reflective surfaces

Applications of 3D Vision

• Robotics 3D vision enables robots to perceive and interact with their environment for tasks such as navigation, object recognition, and manipulation
• Autonomous vehicles 3D vision is used for obstacle detection, road segmentation, and localization to enable safe and efficient navigation
• Augmented and virtual reality (AR/VR) 3D vision techniques allow for the seamless integration of virtual objects into real-world scenes and the creation of immersive virtual environments
• Medical imaging 3D reconstruction from medical scans (e.g., CT, MRI) helps in diagnosis, treatment planning, and surgical guidance
• Industrial inspection 3D vision is used for quality control, defect detection, and dimensional measurements in manufacturing processes
• Entertainment and gaming 3D vision techniques are used for motion capture, character animation, and creating realistic 3D environments in movies and video games
• Cultural heritage preservation 3D reconstruction is used to digitize and preserve historical artifacts, monuments, and sites for documentation and virtual exploration
• Remote sensing 3D vision techniques are applied to satellite and aerial imagery for terrain mapping, urban planning, and environmental monitoring

Challenges and Future Directions

• Robustness and reliability Developing 3D vision algorithms that can handle a wide range of real-world conditions, such as varying illumination, occlusions, and complex scenes
• Efficiency Improving the computational efficiency of 3D vision algorithms to enable real-time processing on resource-constrained devices (e.g., embedded systems, mobile devices)
• Scalability Extending 3D vision techniques to large-scale environments and datasets, such as city-scale 3D reconstruction and global localization
• Semantic understanding Integrating 3D vision with machine learning techniques to enable higher-level understanding of scenes, such as object recognition, scene parsing, and activity recognition
• Multi-modal fusion Combining 3D vision with other sensing modalities (e.g., depth sensors, inertial measurement units) to improve the accuracy and robustness of 3D perception
• Unsupervised and self-supervised learning Developing 3D vision algorithms that can learn from unlabeled or partially labeled data to reduce the reliance on manually annotated datasets
• Domain adaptation Adapting 3D vision algorithms trained on one domain (e.g., synthetic data) to work effectively in different domains (e.g., real-world scenes)
• Interpretability and explainability Developing 3D vision algorithms that can provide meaningful explanations of their predictions and decisions to enhance trust and transparency