Semantic Segmentation of Large-Scale Point Clouds by Encoder-Decoder Shared MLPs with Weighted Focal Loss
Bibliographic record
Abstract
It is essential to do the semantic segmentation task on large-scale outdoor point clouds under the demand of autonomous driving and other applications. Because there is a serious imbalance among different semantic classes, it become a challenging problem. In this paper, we propose a point-based encoder-decoder shared multi- layer perceptrons (MLPs) network with weighted focal loss for semantic segmentation of large-scale point clouds. In the proposed network, we design a residual encoding block which is composed of a relative position encoding block and two neighbor features gathering and combined pooling blocks to aggregate rich neighboring points information. To alleviate the categories imbalance problem, we adopt class-balanced sampler to get the input point cloud block per iteration and use weighted focal loss in the training process. We conducted the experiments on Toronto-3D dataset and the results show that our method achieved an overall accuracy (OA) with 95.70%, a mean intersection over union (mIoU) with 71.85% when input data only contains coordinate information, and an OA with 97.81 %, a mIoU with 81.16% when input data contains both of coordinate and color information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".