Forum Discussion
Home decor platform development
My customer is developing a home decor platform where users can upload images of their rooms, apply different decor items like furniture or wall colors, and visualize how they would look. The platform relies on AI tools and APIs for object recognition and placement but is facing issues with accurately identifying the floor and objects.
The AI is currently rejecting the floor or placing items incorrectly. They are also working on improving search results based on uploaded images, which show similar but not exact matches, leading to inconsistencies in user experience. They are facing challenges in achieving accuracy and reducing processing time.
2 Replies
AI-based home décor tools often struggle with identifying floor areas correctly, especially when room angles or lighting vary. Better segmentation models and floor plane detection can help improve object placement. Also, using CLIP-based visual search can return more accurate décor matches in less time. If processing is slow, shifting heavy tasks to cloud inference can optimize performance and improve UX.
hi Azliza5873 This is a very common (and honestly very hard) problem space, so you’re not alone here A few thoughts based on similar implementations we’ve seen in image-based visualization platforms:
1.Floor detection issues
Generic object detection models usually struggle with “implicit” objects like floors and walls because they don’t have clear boundaries.
What tends to work better:
- Use semantic segmentation instead of object detection for floors/walls (pixel-level labeling rather than bounding boxes).
- If you’re using Azure services, Azure AI Vision (Image Analysis / Segmentation) combined with a custom-trained model often performs much better than out-of-the-box detection.
- Add pre-processing steps:
- Perspective correction
- Depth estimation (even monocular depth models help a lot)
- Enforcing scene constraints (e.g., floor is always bottom-most contiguous region)
Many platforms also apply rule-based post-processing (for example: “never place furniture floating above detected floor plane”).
2.Incorrect object placement
This is usually less about recognition and more about scene understanding.
Recommendations:
- Introduce a layout inference step: detect room type (living room, bedroom, etc.) and constrain placement rules accordingly.
- Use 2D + depth hybrid logic:
- Estimate depth → infer scale → snap objects to detected planes (floor/wall).
- If accuracy matters more than automation, consider a human-in-the-loop fallback for low-confidence detections (even optional user confirmation improves UX a lot).
3.Image-based search returning “similar but wrong” results
This is a classic embedding vs intent gap.
Improvements you can try:
- Move from pure visual similarity to multimodal embeddings (image + metadata like style, color, room type).
- Cluster decor items by style taxonomy (modern, rustic, minimal, etc.) and filter search results using those tags.
- Apply a confidence threshold: if similarity score is below X, don’t show results (better fewer results than confusing ones).
Azure AI Search + vector search works well here, especially when combined with custom embeddings tuned on your decor catalog.
4.Performance and processing time
A few practical optimizations:
- Split the pipeline:
- Fast, lightweight model for initial detection
- Heavier models only when confidence is low
- Cache embeddings and segmentation outputs aggressively.
- Resize images early and only run high-res processing on selected regions (ROI-based processing).
5.Big-picture advice
Trying to solve recognition + placement + realism with a single AI step usually leads to frustration. The most successful platforms treat this as a pipeline of specialized steps with guardrails and heuristics in between.
If you’re aiming for production quality, expect:
- Custom model training
- Hybrid AI + rules
- Occasional user guidance
That combination typically delivers far better accuracy and user trust than “fully automatic” approaches.
Hope this helps.
