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For voxel resolution, we chose 3 cm along the x and y axes and 30 cm along the z axis. More specifically, we chose H, W, and Z to be 512, 512, and 12 respectively.
Each voxel is a float number between 0 and 1, and each represents the density of point clouds that fall within that voxel. This input is created by projecting every point cloud and its associated semantic vector to a 2D bird’s-eye view of the image grid.įor z-slicing, we map the points into an HxWxZ voxel space, where Z is the number of slices along the gravity direction. The semantic map is an HxWxK vector that encodes the semantic information of the space from the bird’s-eye view, where H, W, and K are image length, width, and number of semantic classes respectively. Our wall and openings detection pipeline first converts the input semantic point clouds to two pseudo-image representations: The lines are then lifted to 3D using our postprocessing pipeline, which leverages the estimated wall height. RoomPlan predicts the walls and openings as 2D lines via our end-to-end line detector neural network. Room Layout Estimation Walls and Openings Figure 1: The figure shows the room layout estimation (RLE) process, starting with the walls and openings pipeline.
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In the second stage, the detected 2D walls and openings are lifted into 3D using a series of postprocessing algorithms that leverage the wall height. In the first stage, we use a neural network that takes the point clouds along with their semantic labels (for simplicity, we will call this semantic point clouds throughout this article) and predicts the 2D walls and openings in a bird’s-eye view of the scene.
Detection of Walls and OpeningsĪs shown in Figure 1, we have designed a two-stage algorithm. The pipeline consists of two main neural networks: One is in charge of detecting walls and openings, and the other is responsible for detecting doors and windows. To tackle these challenges, we designed a pipeline that we describe in detail below.
This task is inherently challenging due to factors such as variations in room size, furniture that might block structures or openings (furniture occlusion), as well as the presence of plate glass and mirrored walls. RLE detects walls, openings, doors, and windows, then processes the data on the user’s iPhone or iPad in real time, as the device camera scans the environment. Room Layout EstimationĪ fundamental component of RoomPlan is room layout estimation (RLE). In this article, we cover these two main 3D components in more detail. The 3D object-detection pipeline recognizes 16 object categories directly in 3D, covering major room-defining furniture types, such as sofa, table, and refrigerator.
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It detects doors and windows on 2D wall planes and later projects them into 3D space, given the wall information and camera position. The estimator detects walls and openings as lines and lifts them into 3D using estimated wall height. The 3D room layout estimator leverages two neural networks, one that detects walls and openings, and another that detects doors and windows. The resulting room capture is provided as a parametric representation and can be exported to various Universal Scene Description (USD) formats, including USD, USDA, or USDZ.Īt the heart of RoomPlan are two main components:Īn example of the scanning process from an iPhone user's point of view for a kitchen is shown in Video 1. Easy-to-understand prompts guide the user to better capture (or scan) planes and objects in the room at the right speed, lighting, and distance.
#Serial number search box shot 3d 3.6 software#
Powered by ARKit and RealityKit software frameworks for developing augmented reality games and applications, RoomPlan is a new Swift API that uses the camera and LiDAR Scanner on iPhone and iPad to create a 3D floor plan of a room, including dimensions and types of furniture. To address automatic 3D floor-plan generation, Apple released RoomPlan in 2022. Among these problems, creating a 3D floor plan is becoming key for many applications in augmented reality, robotics, e-commerce, games, and real estate. A variety of methods are addressing different parts of the challenge, like depth estimation, 3D reconstruction, instance segmentation, object detection, and more. Fundamental research in scene understanding combined with the advances in ML can now impact everyday experiences.
More recently the release of LiDAR sensor functionality in Apple iPhone and iPad has begun a new era in scene understanding for the computer vision and developer communities. 3D Scene understanding has been an active area of machine learning (ML) research for more than a decade.
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