mlsanityData to 7 Oct 2026

Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion

arXiv 2303.11720 ↗First posted 21 Mar 2023cs.LG cs.CV cs.ROcited by 1

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Abstract (arXiv)

Lidar is a vital sensor for estimating the depth of a scene. Typical spinning lidars emit pulses arranged in several horizontal lines and the monetary cost of the sensor increases with the number of these lines. In this work, we present the new problem of optimizing the positioning of lidar lines to find the most effective configuration for the depth completion task. We propose a solution to reduce the number of lines while retaining the up-to-the-mark quality of depth completion. Our method consists of two components, (1) line selection based on the marginal contribution of a line computed via the Shapley value and (2) incorporating line position spread to take into account its need to arrive at image-wide depth completion. Spatially-aware Shapley values (SaS) succeed in selecting line subsets that yield a depth accuracy comparable to the full lidar input while using just half of the lines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 21 Mar 2023.

Builds on

16 of 23 references.

References · 16

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  12. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
4 more
  1. 2020Adaptive Context-Aware Multi-Modal Network for Depth Completioncited by 69
  2. 2021Unsupervised Depth Completion with Calibrated Backprojection Layerscited by 61
  3. 2019Dense Depth Posterior (DDP) from Single Image and Sparse Rangecited by 57
  4. 2017Towards better understanding of gradient-based attribution methods for Deep Neural Networkscited by 51

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Citing papers per year: 2024: 1.

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  1. 2024Shapley Pruning for Neural Network Compressioncited by 2