r/LiDAR

I have a Matterport PRO3 but I'm finding the speed at which i need to operate using a tripod-mounted scanner is too slow. So I've been looking into SLAM scanners, in particular the FJD P2 which I am considering purchasing.

One big question I can't seem to find a definitive and reliable answer to: is an LOD 200 scan of a building possible to produce consistently with the P2? I'd like to be able to get scans similar to the point cloud accuracy I get with my Matterport PRO3 but in a fraction of the time.

Hey everyone
I’m a final-year Robotics & Automation student in Mangalore and I’m working on my major project right now.
I’m trying to get my hands on a LiDAR sensor — doesn’t have to be brand new or anything fancy. If anyone has one lying around, or knows someone who’s selling/renting a used one, that would be super helpful.
I’m totally okay with borrowing/renting or buying a second-hand unit, especially something that works with ROS2 / robotics projects.
If you’ve got any leads, please drop a comment or DM me

The appeal is: no debt, shrinking losses, 43% gross margins, enough cash to reach profitability without dilution, native color integration, and diversified clients not just AV but also robotics, smart infrastructure, agriculture, etc

I’m assuming LiDAR costs will drop over time. What am I missing in terms of other advantages oust has over competitors?

What’s the bear argument for $oust and/or Lidar?

What Exactly Happened Today — The Verified Facts

Aeva launched an Optical Connectivity business, expanding its proprietary high-power optical source and silicon photonics technology to next-generation AI data centers. A leading provider of high-speed optical engines for AI data centers signed a joint development agreement to integrate Aeva’s high-power optical sources into a new module for deployment by a major hyperscaler, with initial deployment targeted for H2 2027 and production ramp targeted for 2028.

Aeva’s SOA demonstrates optical output power exceeding 28 dBm while achieving wall-plug efficiencies greater than 20% at temperatures up to 50°C — a combination of high power, efficiency, and thermal robustness that drives lower system power dissipation, reduced costs, and significantly improves reliability in large-scale AI data center deployments.

Q2 2026 financial results: Revenue $6.1M (slightly below Q1’s $6.3M but within guidance trajectory). Full year 2026 guidance of $30-36M maintained. CFO transition announced — Saurabh Sinha transitioning, new CFO joining with 20+ years finance experience.

Why This Is Architecturally Different From Everything Else Aeva Has Announced

This is not an incremental win. Let me explain precisely why, because most retail investors will miss the magnitude of what happened today.

The core insight: Aeva spent a decade solving one of the hardest problems in physics — making coherent light sources that are simultaneously high-power, low-noise, thermally stable, and manufacturable at automotive-grade reliability. They did this because FMCW lidar requires it. No one else was willing to invest this deeply in photonics infrastructure because the automotive lidar market was too small and too uncertain to justify it.

The result: Aeva accidentally built what may be the world’s most capable high-power silicon photonics platform — and they built it to a reliability standard (automotive grade) that far exceeds what data center applications require.

Now the data center industry has a desperate, urgent need for exactly this technology. The analogy: Aeva is like a company that spent a decade building the world’s most reliable industrial pump for deep-sea oil drilling — and then discovered that the same pump, with minimal modification, is exactly what every hospital in the world needs for a new critical medical procedure. The R&D is already paid for. The manufacturing is already industrialized. The marginal cost of entering the new market is a fraction of what it would cost anyone else to start from scratch.

The NPO/CPO Market — Why It’s Enormous

The near-packaged optics market was valued at $3.8 billion in 2025 and is projected to reach $18.6 billion by 2034, growing at a CAGR of 19.3%.

The global co-packaged optics market is projected to grow from $328.6 million in 2026 to $3.374 billion by 2034, exhibiting a CAGR of 33.8%.

Hyperscaler firms aim to deploy clusters with over 1 million GPUs by 2027. These clusters demand extremely fast and low-latency interconnections, creating an absolute necessity for co-packaged optics technology.

The combined NPO + CPO market reaches approximately $22 billion by 2034. The laser source component — Aeva’s specific entry point — represents roughly 20–30% of total module value. That’s a $4–6 billion laser source TAM within the broader NPO/CPO market.

The Competitive Landscape — Where Does Aeva Actually Fit?

NVIDIA made strategic investments in Coherent and Lumentum as laser supply chain partners. Ayar Labs joined NVIDIA’s NVLink Fusion ecosystem. Marvell completed its Celestial AI acquisition. The key competitors: Broadcom, Intel, NVIDIA, Cisco, Lumentum, Coherent, Ayar Labs, Marvell.

These are $10B–$500B companies. Why would a hyperscaler work with $1B market cap Aeva instead?

The answer is specific and technical: Aeva’s SOA achieves optical output power exceeding 28 dBm at wall-plug efficiencies greater than 20% at temperatures up to 50°C. This performance profile — particularly the thermal efficiency at high temperatures — is precisely what data centers need as they struggle with power density and cooling constraints. Large incumbents (Lumentum, Coherent) make excellent lasers but haven’t been forced to optimize for automotive-grade thermal robustness. Aeva has.

Large-scale NVIDIA CPO production could slip to 2028–2029 on systems-engineering grounds — serviceability, reliability, and manufacturing-test yield — elevating near-package optics (NPO) as a pragmatic intermediate. This timing actually helps Aeva — NPO is the near-term transition architecture, and that’s exactly where Aeva signed its first customer agreement.

Hey everyone
I’m a final-year Robotics & Automation student in Mangalore and I’m working on my major project right now.
I’m trying to get my hands on a LiDAR sensor — doesn’t have to be brand new or anything fancy. If anyone has one lying around, or knows someone who’s selling/renting a used one, that would be super helpful.
I’m totally okay with borrowing/renting or buying a second-hand unit, especially something that works with ROS2 / robotics projects.
If you’ve got any leads, please drop a comment or DM me

During a research internship, I ran into a problem involving massive neighbor queries on a GPU for a large particle-dynamics simulation. This led me to experiment with and develop SquareNet, an open-source Python package for NumPy/JAX/PyTorch.

https://preview.redd.it/vb8vpyi421ih1.png?width=705&format=png&auto=webp&s=9de4e42838ca21cd08571c30c1c46c239809e8f9

Its core sorting algorithm (Cartesian sort) enables fast, greedy multidimensional reordering of raw point sets — essentially a form of gridification. Raw points, e.g. (x, y, z, ...), are mapped to unique grid multi-indices [i, j, k, ...] while trying to preserve local geometry, somewhat like a multidimensional generalization of a space-filling curve.

The collection of all multi-indices forms a grid lattice that can be processed efficiently with ML tensor-based frameworks, even when the initial dataset is an irregular point cloud.

I’m wondering whether this could be useful in contexts such as convolutional networks, non uniform fourier transform or ANN search on irregular LiDAR data.

The target use case is approximate but fast and scalable assignment preprocessing, then the grided/tensorized version of the dataset is exploited by standard tensor based frameworks, and result is converted back to the points. High-quality procedures for the assignement part already exist and are well established, such as optimal transport, but they were intractable in my context due to their O(N²/N³) complexity. Cartesian sort, by contrast, runs in O(N log N). It is specifically designed for grid assignment in a greedy setting, trading global optimality for speed and scalability.

In my practical application, involving millions of points processed in a dynamic context (Gaussian blue noise), this simple approach turned out to work well: it provided a ~100× speedup compared to exact brute-force computation of particle interactions, with negligible approximation error (I can provide more details about this experiment if useful).

One caveat is that a single gridification pass introduces a slight axis bias and can produce some distortion/outliers, which can be problematic for challenging distributions where exact geometric precision is required. If exact accuracy is critical, one possible approach would therefore be to build an ensemble of gridifications, each using a different viewpoint/rotation.

Empirically, something like 8 randomly chosen viewpoints seems to give near-perfect recovery of local geometry in a 3D test evaluated with a freud analysis (second link below). However, in the Gaussian blue noise context, where the geometry is smoother, a single viewpoint was already sufficient.

I built an interactive demo on Hugging Face (first link below) to showcase the approach. I’d really appreciate any feedback, especially on whether this idea has already been explored in related computer vision / point-cloud literature, or whether you see potential applications or obvious better alternatives that I may be missing.

interactive HF demo

3D exact nn query discussion