Stereo-LiDAR Fusion

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

8 shown of 8 papers with code (11 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Pyramid Stereo Matching Network
  23 Mar 2018 6 repositories listed Syntology ran 3 of 11 samples · 8 unverified
  The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume.
• End-to-End Learning of Geometry and Context for Deep Stereo Regression
  13 Mar 2017 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images.
• Learning Guided Convolutional Network for Depth Completion
  3 Aug 2019 2 repositories listed
  It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion.
• Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensification
  7 Apr 2025 1 repository listed
  We present a real-time, non-learning depth estimation method that fuses Light Detection and Ranging (LiDAR) data with stereo camera input.
• Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions
  30 Mar 2022 1 repository listed
  The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving.
• Non-Local Spatial Propagation Network for Depth Completion
  20 Jul 2020 1 repository listed Syntology ran 1 of 5 samples · 4 unverified
  In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion.
• Scene Completeness-Aware Lidar Depth Completion for Driving Scenario
  15 Mar 2020 1 repository listed
  Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as KITTI, do not provide groundtruth for upper areas.
• 3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
  5 Apr 2019 1 repository listed
  The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.