Papers › Depth-aware CNN for RGB-D Segmentation
Depth-aware CNN for RGB-D Segmentation
Weiyue Wang, Ulrich Neumann
Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high computation and memory cost. To address these issues, we present Depth-aware CNN by introducing two intuitive, flexible and effective operations: depth-aware convolution and depth-aware average pooling. By leveraging depth similarity between pixels in the process of information propagation, geometry is seamlessly incorporated into CNN. Without introducing any additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1803.06791")
Code
Syntology Ran 1 of 11 code samples harvested from 2 repositories linked to this paper; 10 have no recorded run. Of those that ran: 1 ran · our draft was wrong.
By repository: official repository: 8 samples from 1 repository, 1 ran; community (archive-listed): 3 samples from 1 repository, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
11 samples harvested; 1 ran; 0 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 0 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.
Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.
098a8251265f82c2 · report
2550125e25acce91 · report
8a7cda981e891b1b · report
c47ae52360c6b10a · report
48d8d1e1855aa6dd · report
b0ad13db5a53e1a2 · report
b6bdfbe93eac1645 · report
fa8c10bd6c94939c · report
aa4b1a72a86182e3 · report
72271df9d7f8f6ad · report
8c31f5f90a12d378 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | NYU Depth v2 | Depth-aware CNN | Mean IoU | 43.9% | #98 of 121 | Archive leaderboard | report |
| Semantic Segmentation | SUN-RGBD | TokenFusion (S) | Mean IoU | 42.0% | #42 of 44 | Archive leaderboard | report |
| Semantic Segmentation | Stanford2D3D - RGBD | Depth-aware CNN | Pixel Accuracy | 65.4 | #6 of 6 | Archive leaderboard | report |
| Semantic Segmentation | Stanford2D3D - RGBD | Depth-aware CNN | mAcc | 55.5 | #6 of 6 | Archive leaderboard | report |
| Semantic Segmentation | Stanford2D3D - RGBD | Depth-aware CNN | mIoU | 39.5 | #6 of 6 | Archive leaderboard | report |
| Thermal Image Segmentation | MFN Dataset | Depth-aware CNN | mIOU | 46.1 | #46 of 55 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections