Upsampling Indoor LiDAR Point Clouds for Object Detection
Bibliographic record
Abstract
As an emerging technology, LiDAR point cloud has been applied in a wide range of fields. \nWith the ability to recognize and localize the objects in a scene, point cloud object detection \nhas numerous applications. However, low-density LiDAR point clouds would degrade the \nobject detection results. Complete, dense, clean, and uniform LiDAR point clouds can \nonly be captured by high-precision sensors which need high budgets. Therefore, point \ncloud upsampling is necessary to derive a dense, complete, and uniform point cloud from \na noisy, sparse, and non-uniform one. \n \nTo address this challenge, we proposed a methodology of utilizing point cloud upsam pling methods to enhance the object detection results of low-density point clouds in this \nthesis. Specifically, we conduct three point cloud upsampling methods, including PU-Net, \n3PU, and PU-GCN, on two datasets, which are a dataset we collected on our own in an \nunderground parking lot located at Highland Square, Kitchener, Canada, and SUN-RGBD. \nWe adopt VoteNet as the object detection network. We subsampled the datasets to get \na low-density dataset to stimulate the point cloud captured by the low-budget sensors. \nWe evaluated the proposed methodology on two datasets, which are SUN RGB-D and \nthe collected underground parking lot dataset. PU-Net, 3PU, and PU-GCN increase the \nmean Average Precision (under the threshold of 0.25) by 18.8%,18.0%, and 18.7% on the \nunderground parking lot dataset and 9.8%, 7.2%, and 9.7% on SUN RGB-D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".