How AI sees a flat's contents and calculates its volume
At Movin AI we apply computer vision in logistics for move volume, to turn property assessment into an automatic, fast, accurate process. Our technology analyses two-dimensional images, identifies furniture in a home and calculates the real cubic metres of each room during the sales visit.
What is computer vision applied to moving logistics?
Computer vision is a field of artificial intelligence that trains computer systems to extract structured data, interpret and understand the visual environment from digital images or videos. In logistics applied to household transport, this technology turns photos or videos into a precise volumetric inventory in seconds.
Unlike the human eye or a salesperson's subjective judgement, a computer-vision system processes each frame extracting geometric patterns, object boundaries and spatial relationships. That automates the most critical and error-prone phase of a move: measuring volume.
Instead of estimating by eye how many cubic metres the furniture of a property will occupy, computer vision processes the scene in three automated steps:
Segmentation and detection: recognises each piece in the room individually.
Semantic classification: assigns a specific category to the item (for example: 3-seater sofa, dining table, standard box).
Volumetric estimate: infers spatial dimensions to determine the total cubage it will occupy in the transport truck.
By integrating this technology into sales operations, transport companies remove quote uncertainty, optimise fleet space and avoid operational penalties from load-calculation errors.
Object-detection models: how AI classifies furniture
Applying computer vision to move-volume logistics lets us identify and catalogue items in real time during the sales visit, turning unstructured visual data into a volumetric inventory ready to quote. Through deep-learning algorithms, our software analyses the space in 2D, isolates each piece of furniture and assigns it a precise category with matching cubage.
YOLO architecture and convolutional neural networks (CNN)
To automate inventory estimates, at Movin AI we implement state-of-the-art detection architectures based on YOLO (You Only Look Once) models combined with convolutional neural networks (CNN). Unlike traditional sequential methods, YOLO processes the full image in a single computational pass.
That lets us generate bounding boxes over multiple objects in milliseconds. The CNN processes the pixel matrix to extract key visual features such as edges, textures and geometries. When the salesperson scans a room, the system simultaneously recognises sofas, tables or appliances, speeding inventory creation.
Training the network on furniture-sector datasets
A generic model does not solve the sector's operational needs. That is why we have trained our neural network on proprietary datasets specialised in furniture and household items. This fine-tuning lets the algorithm understand the design and proportion variations found in any home.
This computer-vision development for move-volume logistics brings key operational advantages:
Precise semantic classification: the system distinguishes specific types (a three-seater sofa versus an armchair), assigning the right cubic metres to each piece.
Detection of bundles and stacks: it identifies stacked cardboard boxes or groups of mixed objects, critical for calculating the real space needed in the truck.
Filtering complex geometry: it recognises irregular shapes or reflective materials (glass, mirrors), cutting false positives sharply.
Thanks to that specialisation we remove the subjectivity of a manual inventory and base operations on accurate data from the first sales visit.
From 2D space to volume in m³: methodology for volumetric calculation
To estimate real truck space we apply computer-vision algorithms for calculating volume on a move that turn two-dimensional photos or videos into precise cubage metrics. Our technology analyses the 2D scene, infers each object's spatial dimensions and calculates total cubic metres with no manual measuring.
Depth estimation and 3D bounding boxes
The first technical challenge is extracting the third dimension from flat images. At Movin AI we solve depth loss with neural networks trained to predict monocular depth maps and estimate three-dimensional bounding boxes.
The system identifies the vertices of each piece of furniture and projects a virtual 3D parallelepiped onto the 2D image. Combining detected proportions with environmental references (floor or walls), the AI calculates the real height, width and depth of each piece in the home.
Cubage algorithms and dynamic cubing tables
Once estimated dimensions are obtained, the software processes the data with cubage algorithms developed for transport logistics. Instead of depending on static calculations, our system crosses spatial information with dynamic cubing tables.
Adjustment by type: detected geometry is compared with standardised furniture patterns to correct perspective deviations.
Net volume calculation: the exact metric space each item occupies (m³) is determined.
Aggregation by room: total cubage per room is summed to project the load capacity required in the fleet.
This automated flow lets salespeople issue accurate quotes during the digital visit, reducing estimate errors and improving operations.
Technical challenges of the domestic environment: occlusions, perspective and variability
Residential environments present complex variables that make automatic visual measurement harder. Applying computer vision in logistics to measure move volume means resolving spatial interference that distorts images. At Movin AI we designed our architecture to overcome three critical obstacles during the sales visit:
Blind spots and overlapping objects (occlusions)
In a home it is common to find chairs under tables or stacked boxes that hide furniture outlines. To avoid inventory errors, our software uses contextual inference networks. When it detects a visible fragment of an object, the algorithm reconstructs the hidden geometry and calculates the real cubic metres of the complete piece.
Variable lighting and lens distortion
Digital visits happen in spaces with poor light or sudden exposure changes. We apply dynamic processing to each frame to correct lens aberration and stabilise lighting. That guarantees the AI segments each surface accurately, whatever the room conditions.
Perspective variability in small spaces
Perspective in narrow rooms distorts the perceived size of objects. Our algorithms calculate camera tilt and vanishing point to adjust scale relative to the floor. That turns pixels into reliable metric dimensions, removing deviations in the final quote.
How we solved cubage calculation at Movin AI
At Movin AI we built Movin Vision to integrate specialised computer-vision algorithms for move-volume logistics. Our platform combines real-time processing with operational assists that guarantee accuracy on every visit.
The Movin Vision inference pipeline
Our architecture processes each image in three phases:
Capture and filtering: we clean the frame and adjust lighting.
Detection and 3D inference: we identify objects, draw three-dimensional bounding boxes and estimate cubic metres per room.
Assisted validation: the salesperson confirms on-screen suggestions in seconds, removing manual-measurement errors and speeding quote preparation.
Frequently asked questions about computer vision in moving
What accuracy does computer vision offer in logistics for measuring move volume?
Our algorithm reaches more than 95% accuracy on total cubage estimates. Combining 3D bounding boxes with dynamic cubing tables, we remove the deviations of manual visual calculation.
Does the app need a LiDAR sensor to measure the home?
No. Movin Vision processes 2D images and videos taken from any conventional smartphone. The neural network infers monocular depth without specialised hardware.
How does the system process data if there is no internet during the sales visit?
The inference model runs locally on the phone. It generates the inventory and calculates volume offline, syncing the quote with the CRM when it recovers the network.
Automate the operational accuracy of your sales visits with Movin AI
Calculating cubic metres is no longer a manual estimate subject to human error. Our computer-vision technology lets your sales team calculate exact volumes, speed up quotes and optimise load capacity on every job.
If you want to digitise operations and cut operating costs, get in touch and request a technical demonstration.
