Papers › RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

4 Jun 2018arXiv:1806.01054archive 2025-07-28

Jindong Jiang, Lunan Zheng, Fei Luo, Zhijun Zhang

Indoor semantic segmentation has always been a difficult task in computer vision. In this paper, we propose an RGB-D residual encoder-decoder architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet, the residual module is applied to both the encoder and decoder as the basic building block, and the skip-connection is used to bypass the spatial feature between the encoder and decoder. In order to incorporate the depth information of the scene, a fusion structure is constructed, which makes inference on RGB image and depth image separately, and fuses their features over several layers. In order to efficiently optimize the network's parameters, we propose a `pyramid supervision' training scheme, which applies supervised learning over different layers in the decoder, to cope with the problem of gradients vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.

PaperPDFCodeCode Syntology ran

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="1806.01054")

Code

Syntology Ran 0 of 9 code samples harvested from 1 repository linked to this paper; 9 have no recorded run.

By repository: community (archive-listed): 9 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.

JindongJiang/RedNet officialmentioned in papermentioned on GitHubpytorch report
lyqcom/rednet30 mindsporeApache-2.0 report

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

9 samples harvested; 0 ran; 0 honoured the contract we drafted; 9 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.

9unverified

Licence: 0 of the 9 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 lyqcom/rednet30. “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.

PSNR lyqcom/rednet30/postprocess.py community (archive-listed) unverified Apache-2.0 (permissive) · 72009a8d2189cd89 · report
calc_psnr lyqcom/rednet30/Initial/metrics.py community (archive-listed) unverified Apache-2.0 (permissive) · 22b06bae8ffd3393 · report
padding lyqcom/rednet30/preprocess.py community (archive-listed) unverified Apache-2.0 (permissive) · 90bc913587ce31f3 · report
quantize lyqcom/rednet30/Initial/metrics.py community (archive-listed) unverified Apache-2.0 (permissive) · aa9d5683a00262dc · report
read_bin lyqcom/rednet30/postprocess.py community (archive-listed) unverified Apache-2.0 (permissive) · dfc839ac35cda175 · report
read_bin_as_hwc lyqcom/rednet30/postprocess.py community (archive-listed) unverified Apache-2.0 (permissive) · 93924d458dad1818 · report
rgb2ycbcr lyqcom/rednet30/Initial/metrics.py community (archive-listed) unverified Apache-2.0 (permissive) · d39f1e44595b5975 · report
search lyqcom/rednet30/Initial/common.py community (archive-listed) unverified Apache-2.0 (permissive) · 7c1f6cd7e279ed21 · report
set_channel lyqcom/rednet30/Initial/common.py community (archive-listed) unverified Apache-2.0 (permissive) · 8c2264f588db0c23 · report

Tasks

DecoderSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Segmentation NYU Depth v2 RedNet Mean IoU 47.2% #86 of 121 Archive leaderboard report
Semantic Segmentation SUN-RGBD TokenFusion (Ti) Mean IoU 47.8% #34 of 44 Archive leaderboard report
Semantic Segmentation THUD Robotic Dataset RedNet mIoU 76.92 #3 of 4 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.

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