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Hi everyone,
This question builds on the thread here, but I thought it would be helpful to start a new one for clarity. My goal is not to make an impractically broad feature request but rather to understand the challenges and explore if there are ways to handle them myself. To summarize the linked discussion: for scanning extensive galleries or corridors, ideally, we’d like to capture the ceiling, floor, and walls accurately with a single, smooth pass. To maximize coverage, I’ve been using the “low confidence” setting in the app, which reduces data discard but introduces substantial noise because of depth interpolation. This is especially problematic in low-texture areas. An example of this noise can be seen below: Although this screenshot doesn’t fully show the extent, the noise is problematic in practice, and after extensive attempts to create a usable mesh from these clouds, I had to set the project aside. Using “medium confidence” reduces noise slightly, but it seems the only consistently viable option is “high confidence,” as suggested by Mathieu in the original thread. However, this setting significantly extends scan time, as each pass needs to be carefully deliberate. In contrast, it seems that other apps have found effective ways to mitigate this. For instance, here’s a quick scan I captured using Scaniverse while walking briskly through a low-texture gallery: Given the high quality achieved at such a speed, I’m curious about how Scaniverse handles this (it’s unfortunately closed-source) and whether any insights from it might make RTAB-Map more adaptable to similar scanning workflows. I would guess that there is that is some clever playing around with the confidence maps, though I’m not sure of the specifics. Here are a few questions I hope might clarify a path forward: - Are there any known algorithms or public methods relevant to handling this type of noise, or is it likely a custom solution developed by the Scaniverse team? - Is there a way to achieve comparable results with RTAB-Map currently? Specifically, can it produce reliable scans while moving quickly through large, low-texture corridors? - If not, are there strategies I could try using RTAB-Map’s data output to improve results? By that I mean, starting from the depth images and poses (and RGB on the side) and then using external hand-made code. Any suggestions on where to start would be greatly appreciated. Although I fear that it will be hard to do anything consistant and reliable without the data of the confidence maps. Thank you in advance for any insights or suggestions! Best, Pierre |
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After a quick comparison with Scaniverse and rtabmap's depth confidence settings, it seems Scaniverse is using high confidence setting. Here is a single frame taken from both apps of the same thing (left=Scaniverse, right=RTAB-Map with high confidence):
In comparison, here RTAB-Map in medium and low confidences respectively: So, the data looks the same. Note sure why you mean by "Given the high quality achieved at such a speed" if both apps use same inputs. Their point cloud rendering looks indeed nicer, don't know if this is just that effect that gives the feeling of giving better results. In term of speed, I noticed that their output file (with raw data) is 4 times larger than RTAB-Map's output file, meaning they are maybe reocrding point clouds faster (5 Hz or even more). In RTAB-Map's Mapping settings, you can increase the frame rate at which we add cloud to the map, so that you can scan "quicker". In term of meshing, they probably use a different approach than in RTAB-Map, though results look pretty similar. We have some noise filtering approaches in Desktop App (primarily based on filters that you can find on PointCloud Library), but we didn't add the options to iOS app. Probably, as they seem recording same data. Note that you can set Point Cloud rendering to see better all points added in real-time. With the online meshing approach, some polygons may not be shown (even if the points are recorded) when angle difference between surface normal and the camera point of view is large (there is a setting to adjust that angle in Rendering options). To try more settings, I generally load the map in RTAB-Map desktop app, then play with all options in Export Clouds dialog. cheers, Mathieu |
Hi Mathieu,
Thank you for your response. It looks like the issue comes from a misunderstanding on my part. I had assumed Scaniverse was doing something beyond depth high-confidence, but it seems I was wrong. I’ll focus more on testing with high confidence as you suggested. There are still some things that are not clear to me about the comparison of Scaniverse vs RTAB's performance for the scanning workflow I was talking about but I will do more experimenting. Apologies for the confusion, and thanks again. Best, Pierre |
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