Spatial AI

Learn how AI algorithms map, understand, and track 3D environments in real time to enable spatially aware AR/VR experiences.

What is Spatial AI?

Spatial AI refers to AI systems that understand the 3D geometry and structure of physical spaces. It combines computer vision, sensor fusion, and deep learning to enable devices to know where they are, what surrounds them, and how the environment is structured - all in real time.

SLAM: Simultaneous Localization and Mapping

SLAM is the foundational technology that allows AR/VR devices to simultaneously track their position and build a map of the environment. Modern SLAM systems use AI to improve robustness and accuracy.

# Conceptual Visual SLAM pipeline
import cv2
import numpy as np

# Feature detection with ORB (fast for real-time)
orb = cv2.ORB_create(nfeatures=1000)
keypoints, descriptors = orb.detectAndCompute(frame, None)

# Feature matching between consecutive frames
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(desc_prev, descriptors)

# Estimate camera pose from matched features
E, mask = cv2.findEssentialMat(pts_prev, pts_curr, camera_matrix)
_, R, t, _ = cv2.recoverPose(E, pts_prev, pts_curr, camera_matrix)

# R = rotation matrix, t = translation vector
# Together they define camera movement between frames

Depth Estimation

Depth estimation gives AR/VR systems the ability to understand how far away objects are. AI enables monocular depth estimation from a single RGB camera, eliminating the need for dedicated depth sensors.

# Monocular depth estimation with MiDaS
import torch

model = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
model.eval()

transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
transform = transforms.small_transform

# Predict depth from a single RGB image
input_batch = transform(image).unsqueeze(0)
with torch.no_grad():
    depth_map = model(input_batch)

# depth_map contains relative depth values
# Closer objects have higher values

3D Reconstruction

AI enables real-time 3D reconstruction of environments, creating mesh representations that AR content can interact with:

• Neural Radiance Fields (NeRFs) - Learn volumetric scene representations from posed images for photorealistic novel view synthesis.
• 3D Gaussian Splatting - Faster alternative to NeRFs using explicit 3D Gaussian primitives for real-time rendering.
• Mesh Reconstruction - Generate triangle meshes from point clouds or depth maps for physics-based interaction.

Spatial Anchoring

Spatial anchors are AI-maintained reference points that persist across sessions, allowing virtual content to stay in place even when the app is restarted: