Comparing 3D Reconstruction Technologies: Photogrammetry vs. LiDAR vs. SfM
As 3D reconstruction becomes more important across industries such as construction, robotics, surveying, digital twins, and field inspection, teams are increasingly faced with the same question: Which capture method should we use?
Three terms come up again and again in that conversation: photogrammetry, LiDAR, and Structure from Motion, or SfM. They are often grouped together as competing approaches, but the reality is slightly more nuanced. Photogrammetry and LiDAR are distinct methods for capturing or reconstructing the physical world, while SfM is more accurately described as a computational technique typically used within photogrammetry workflows.
That distinction matters, because choosing the right approach is not just about technical accuracy. It is also about cost, speed, operating conditions, output quality, and the type of environment being reconstructed. In many real-world workflows, these technologies are not mutually exclusive. They are complementary.
This article breaks down how each one works, where each one performs best, and how to think about the trade-offs.
The three technologies at a glance
Photogrammetry
Reconstructs 3D geometry from many overlapping photographs. Cameras are cheap and produce richly textured, realistic models.
LiDAR
Measures distance with laser pulses to build a dense point cloud. Favored where precise geometry and difficult lighting are in play.
Structure from Motion
Estimates camera positions and sparse 3D structure from overlapping images. Usually the engine inside a photogrammetry pipeline.
Why these terms are often confused
The confusion exists for a simple reason: in many software tools, the workflow starts with SfM and ends with a photogrammetric model. Users may see them as separate approaches because different tools emphasize different stages of the pipeline.
A typical image-based reconstruction workflow might look like this:
Photograph the object or site from many angles with strong overlap between frames.
Use Structure from Motion to estimate camera poses and sparse scene structure.
Expand the sparse result into a dense point cloud.
Build a continuous surface from the dense point cloud.
Project the original images back onto the mesh for a realistic, textured model.
From the user’s perspective, that may all feel like “doing SfM” or “doing photogrammetry,” depending on the tool or community they come from. In practice, they are closely connected.
How photogrammetry and LiDAR stack up
The strongest workflows are often hybrid
In practice, many advanced reconstruction workflows combine LiDAR and photogrammetry rather than choosing only one.
LiDAR can provide stable geometry and scale, while imagery adds color, texture, and visual realism. This hybrid approach is especially powerful in industrial, urban, and infrastructure settings where teams need both dependable spatial structure and a rich visual layer.
For example, a team might use LiDAR to establish accurate geometry in a facility and then fuse that with camera imagery to generate a model that is both measurable and visually intuitive. For robotics and digital twin workflows, that combination can be more valuable than either technology alone.
Which method should you use?
Answer three quick questions and this recommends your starting point: photogrammetry, LiDAR, or a hybrid workflow.
Where Niantic Spatial fits
Niantic Spatial does not fit neatly into just one of these buckets. The company is best understood as building a broader reconstruction and spatial intelligence platform that draws from image-based reconstruction, supports LiDAR capture where appropriate, and extends beyond classical outputs into Gaussian splats and VPS maps.
At the reconstruction layer, Niantic Spatial’s pipeline is built on Structure from Motion and Multi-View Stereo, with proprietary depth-estimation models designed to produce high-quality surface reconstructions from standard camera input, including in conditions where conventional photogrammetry often struggles. The platform is also described internally as hardware-agnostic, supporting capture inputs from phones, drones, ROVs, and third-party scanning vendors rather than requiring a single sensing stack.
That means Niantic Spatial is not best positioned as “just photogrammetry,” even though image-based reconstruction is a major part of the foundation. It also is not best positioned as a LiDAR company in the traditional sense. Instead, it sits one level higher: using multiple capture modalities to generate reconstruction outputs that support visualization, inspection, simulation, and localization workflows.
On the product side, Scaniverse makes that positioning more concrete. Scaniverse continues to support LiDAR and photogrammetry modes, while also offering Gaussian splats as a distinct output for photorealistic capture. Its own documentation says splats are the better fit when users want photorealism, lighting fidelity, and capture beyond the 5-meter range of the mobile LiDAR scanner or photogrammetry mode, while meshes remain preferable for exports and precise measurements.
So in the context of this article, the cleanest way to describe Niantic Spatial is this: it builds on the photogrammetry and SfM tradition, can incorporate LiDAR where it helps, and differentiates by turning reconstruction into a larger platform for Gaussian splats, digital twins, and VPS-enabled spatial applications.
Final takeaway
Photogrammetry is an image-based approach that is affordable, flexible, and capable of richly textured models. LiDAR is a sensing method that delivers direct depth measurements and stronger geometric reliability. SfM is the computational technique that helps photogrammetry recover 3D structure from overlapping images.
So the real choice is rarely photogrammetry vs. LiDAR vs. SfM. More often it is which combination of sensing, reconstruction, and output best fits the environment and the task. The most effective teams treat these as tools in one spatial capture toolbox, each with its own strengths and ideal use cases.