KITTI-360

Method

Simultaneous Diffusion Sampling for Conditional LiDAR Generation [SimultaneousSampling]
[Anonymous Submission]

Submitted on 17 Mar. 2024 12:12 by
[Anonymous Submission]

Running time:3 h
Environment:NVIDIA V100

Method Description:
Simple road heuristic generates novel view origins.

Conditional diffusion sampling (R2DM base) is
applied simultaneously
to generate LiDAR scans from these views. This step is
the focus of our paper.

Resultant scans are merged to create point cloud of
scene.

Note: As neither the semantic classifier nor road
heuristic
are the focus of our paper, both could definitely be
improved. Image 1 demonstrates an extreme example of
road estimation failing.
Parameters:
16 novel views generated per input scan.
Latex Bibtex:

Detailed Results

This page provides detailed results for the method(s) selected. For the first 5 test point clouds, we display the original image, the color-coded result and an error image. The error image contains 4 colors weighted by the confidence of the pseudo-ground truth:
red: the pixel has the wrong label and the wrong category
yellow: the pixel has the wrong label but the correct category
green: the pixel has the correct label
black: the groundtruth label is not used for evaluation

Test Set Average

Accuracy Completeness F1 mIoU class
80.37 29.49 43.15 3.88
This table as LaTeX

Test Image 0


Test Image 1


Test Image 2


Test Image 3


Test Image 4





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