Revolutionizing Product Focus: How Realsee Accelerates and Simplifies Capture

What has Realsee done to achieve "efficient and precise construction of digital spaces"? From "1:1 complete replication of spaces and completing the collection of a 100-square-meter space in 15 minutes" to "diverse mining of in-depth spatial information - truly restoring offline spatial information and overlaying diversified extended applications", what supports Realsee's "software-hardware integrated digital space solution"? This article will take you to an in-depth interpretation of Realsee's product capabilities.

Enhancing Product Experience: Visible Collection Results

In product experience, user perception is of utmost importance. To make the collection process faster and operation more convenient, Realsee not only optimizes the collection workflow at both software and hardware levels but also provides users with real-time feedback at every stage of collection and in the preview effects of deliverables. This ensures that the efficiency of collection can be immediately visualized, allowing users to see the results of high-performance data capture in real time.

1. Software-Hardware Integration: From Serial to Parallel Workflow

Taking Realsee’s high-precision LiDAR scanner Galois as an example, a single-point scan generates high-precision point cloud files. Relying on Realsee’s self-developed algorithm capabilities, it automatically creates high-precision 3D models, achieving integrated and automated generation of collection processes and deliverable effects.

To further accelerate collection speed, Realsee has refined the entire spatial data acquisition workflow, introducing block-based parallel processing for scanning, data transmission, and model processing. What is block-based parallel processing? When the data volume exceeds memory capacity, data is divided into manageable blocks for batch loading and processing—this is "block-based" processing. When these blocks are processed simultaneously, it becomes "block-based parallel processing."

In simple terms, parallel processing operates like a super-factory assembly line, executing multiple tasks with high quality and efficiency simultaneously. Through block-based parallel processing, Realsee significantly enhances collection efficiency while ensuring deliverable quality.

2. Seamless Point Capture: Reducing User Perceived Time

Creating a complete digital space often requires capturing data from multiple scan points, especially for ultra-large spaces with thousands of scan points. If there is lag when moving between adjacent scan points, users may perceive the capture process as lengthy and inefficient.

To address this, Realsee has built on its automatic pose stitching capability—already achieving over 99% accuracy—to proactively guide users to move to the next scan point immediately after completing the previous capture. During this transition, the device begins processing and computing data in real time, allowing users to view capture results instantly.

This optimized workflow not only shortens users’ subjective perception of capture time but also significantly reduces overall project acquisition duration. By minimizing downtime between scan points and integrating real-time feedback, Realsee streamlines the digital space creation process, ensuring efficiency without compromising precision.

3. Intuitive Result Visualization: Ensuring Confidence in the Capture Process

"Did I operate correctly?" "Is the scan point positioning accurate?"

"What’s the quality of the capture results?"

These are common questions for users new to the data capture process.

Realsee addresses these concerns through intuitive and clear result visualization. By providing real-time feedback on panoramic previews and point cloud renderings, users gain instant insights into capture quality at every stage. This real-time validation not only ensures operational accuracy but also builds confidence throughout the scanning workflow, allowing users to proceed with assurance—whether verifying scan point placement or assessing final deliverable precision.

4. Optimizing Panoramic Preview Effects

Panoramic image quality is crucial for building authentic digital spaces. Realsee has optimized panoramic previews in resolution and color:

• Higher Resolution: Faster data processing enhances preview image resolution, revealing richer details.
• Enhanced Color: Lightweight adjustments to saturation, contrast, and brightness address dull/muted tones, delivering vivid, layered visuals.

These dynamic optimizations ensure users see clear, true-to-life previews during capture, closely mirroring real-world spaces. This improves preview intuitiveness and ensures panoramic images form a solid basis for 3D modeling and spatial analysis.

5. Optimizing Point Cloud Preview Effects

A point cloud is a collection of data points in space representing 3D shapes or objects—specifically, a set of points on an object’s surface. It contains 3D coordinates, laser reflection intensity, and color information, serving as a critical "bridge" in 3D reconstruction. Point clouds visualize spatial structures through depth information, forming the basis for subsequent steps like meshing and texture mapping to generate 3D models. Previewing point cloud effects thus allows users to assess the scale and structure of an entire digital space.The quality of point cloud previews depends on dimensions such as color richness, density, and uniformity:

• Color Richness: More detailed color information in point clouds leads to more realistic textures and details in 3D models.
• Density: Higher density enhances data precision and resolution, ensuring accurate spatial representation.
• Uniformity: Uneven density—such as excessively dense regions versus sparse or missing data in corners—requires homogenization to maintain consistency.

Realsee optimizes point clouds across these dimensions—color, density, and uniformity—to ensure previews effectively convey spatial structures and details. This process enhances certainty during capture, clarifies final outcomes, and provides users with greater confidence in the accuracy of digital space reconstruction.

Enhancing Stitching for Faster, Higher-Quality Results

Stitching scan points is critical for multi-point capture projects, regardless of scale.

Stitching quality impacts digital space authenticity. Realsee has improved stitching speed and adaptability, ensuring seamless, artifact-free results while boosting efficiency.

This delivers geometric accuracy and visual consistency across points, streamlining workflows for large projects and creating immersive, true-to-life digital environments. Realsee’s stitching advancements reflect its commitment to reliable, high-fidelity digital twins.

1. More Stable and Accurate Stitching in Empty Spaces, Slopes, and Similar Scenes

• Understanding Point Stitching Principles:

Point stitching integrates multi-dimensional data—including images, spatial structures, and IMU (Inertial Measurement Unit) sensor readings—to, to extract features, perform matching calculations, and generate precise pose results. At its core, this process hinges on feature matching, which can falter in low-feature environments, such as empty walls or uniform surfaces.

• Realsee’s Advancement in Challenging Scenarios:

Through dedicated R&D, Realsee has significantly enhanced stitching accuracy in feature-sparse and complex-structured environments, such as sloped terrain (e.g., staircases) and similar-scene setups (e.g., utility rooms). Key improvements include:

• Monocular Depth Estimation Model (Cyclops): Stitching success rate with accuracy improved from 91.55% to 99.21%.
• Galois LiDAR Scanner: Maintains a consistently high stitching accuracy of 99.97%.

These optimizations ensure reliable, artifact-free results even in traditionally challenging scenarios, reinforcing Realsee’s leadership in delivering robust, high-precision digital space solutions.

2. Striving for Instant Stitching: Capture and Stitch in Real Time

To enhance the user capture experience, Realsee aims to deliver both speed and precision in stitching without compromising quality. The challenge lies in optimizing complex computations like data parsing and feature extraction to achieve faster results.

Realsee’s solution combines deep insights into user capture patterns and spatial characteristics to narrow down search ranges efficiently. By precomputing high-frequency features of depth data, the platform accelerates data processing and analysis, all while maintaining stitching accuracy.Performance Improvements:

• Realsee Galois 3D LiDAR Camera: Single-point stitching time reduced from 4s to 1.19s (-70.25%).
• Cyclops Monocular Model: Single-point stitching time reduced from 4.8s to 1.7s (-64.6%).

These optimizations ensure that users experience near-instant stitching—"capture complete, stitching done"—making the digital space creation process more efficient and intuitive. Realsee’s commitment to balancing speed and precision underscores its leadership in advanced spatial data solutions.

Realsee’s Innovation: Transforming Virtual Tour Capture into a Click-and-Move Experience

In the past, creating virtual tours demanded professional panoramic gear, trained personnel, and complex VR software, often involving significant time and labor investments.

Realsee has revolutionized the virtual tour landscape through continuous product development and technological advancement. Today, capturing a virtual tour is effortlessly simple with just "a click and a shift." Whether documenting sprawling commercial environments or cozy residential spaces, Realsee offers tailored solutions that adapt to every scale and requirement, making the process faster, more straightforward, and highly efficient. By merging ease of use with technical precision, Realsee redefines how virtual tour capture should work—accessible to, optimized for speed, and designed to elevate spatial documentation in any context.