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SUN-RGBD val Benchmark (3D Object Detection)
3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.
The archive carries no text for this table; the description above is the archive's text for the task 3D Object Detection. archive 2025-07-28
Over time archive 2025-07-28
The chart needs JavaScript; the table below carries every value.
Direction inferred from the metric name, not from the archive: mAP@0.25 (higher is better), mAP@0.5 (higher is better). Not inferred (points only, no best-so-far line): Inference Speed (s). Points are placed at the row's paper date; 32 of 32 rows carry one.
Results archive 2025-07-28
Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.
| Paper | Code | Ran Syntology | Report | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Point-GCC+TR3D+FF | 69.7 | 54.0 | – | Paper | Code | 2023 | linked, not harvested | report | |
| 2 | TR3D+FF | 69.4 | 53.4 | – | Paper | – | 2023 | no code linked | report | |
| 3 | DEST (based on V-DETR) (TTA) | 69.2 | 52.2 | – | Paper | Code | 2025 | 2 of 10 ran · 8 unverified | report | |
| 4 | V-DETR | 68.0 | 51.1 | – | Paper | Code | 2023 | 10 of 12 ran · 2 unverified | report | |
| 5 | Point-GCC+TR3D | 67.7 | 51.0 | – | Paper | Code | 2023 | linked, not harvested | report | |
| 6 | DeMF | 67.4 | 51.2 | – | Paper | Code | 2022 | linked, not harvested | report | |
| 7 | TR3D (Geo only) | 67.1 | 50.4 | – | Paper | – | 2023 | no code linked | report | |
| 8 | CAGroup3D(Geo only) | 66.8 | 50.2 | – | Paper | Code | 2022 | linked, not harvested | report | |
| 9 | OctFormer | 66.2 | 50.6 | – | Paper | Code | 2023 | 2 of 9 ran · 7 unverified | report | |
| 10 | SPGroup3D(Geo only) | 65.4 | 47.1 | – | Paper | Code | 2023 | linked, not harvested | report | |
| 11 | DEST (based on GroupFree3D) | 65.3 | 48.4 | – | Paper | Code | 2025 | 2 of 10 ran · 8 unverified | report | |
| 12 | TokenFusion | 64.9 | 48.3 | – | Paper | Code | 2022 | 1 of 3 ran · 2 unverified | report | |
| 13 | RepSurf-U | 64.9 | 47.7 | – | Paper | Code | 2022 | 2 of 15 ran · 13 unverified | report | |
| 14 | FCAF3D (Geo only) | 64.2 | 48.9 | – | Paper | Code | 2021 | linked, not harvested | report | |
| 15 | RBGNet(Geo only) | 64.1 | 47.2 | – | Paper | Code | 2022 | linked, not harvested | report | |
| 16 | LCPFormer | 63.2 | 46.2 | – | Paper | Code | 2022 | linked, not harvested | report | |
| 17 | GroupFree3D(Geo only) | 63.0 | 45.2 | – | Paper | Code | 2021 | 9 of 17 ran · 8 unverified | report | |
| 18 | HGNet (Geo only) | 61.6 | – | Paper | – | 2020 | no code linked | report | ||
| 19 | BRNet(Geo only) | 61.1 | 43.7 | – | Paper | Code | 2021 | 1 of 3 ran · 2 unverified | report | |
| 20 | VoteNet (PC-FractalDB) | 60.2 | 35.2 | ✓ | Paper | Code | 2022 | linked, not harvested | report | |
| 21 | H3DNet | 60.1 | 39.0 | – | Paper | Code | 2020 | linked, not harvested | report | |
| 22 | VoteNet (Geo only) | 59.1 | 35.8 | – | Paper | Code | 2019 | 2 of 10 ran · 8 unverified | report | |
| 23 | 3DETR-m | 59.1 | 32.7 | – | Paper | Code | 2021 | 6 of 7 ran · 1 unverified | report | |
| 24 | COG+surface+context | 54.3 | – | Paper | – | 2019 | no code linked | report | ||
| 25 | F-PointNet | 54.0 | 0.12 | – | Paper | Code | 2017 | 2 of 4 ran · 2 unverified | report | |
| 26 | COG+surface | 51.0 | – | Paper | – | 2018 | no code linked | report | ||
| 27 | COG | 47.6 | – | Paper | – | 2016 | no code linked | report | ||
| 28 | 2D-driven | 45.1 | 4.15 | – | Paper | – | 2017 | no code linked | report | |
| 29 | Frustum VoxNet v2 (FPN + 3D ResNetFCN6 V2) | 45.0 | 0.24 | – | Paper | – | 2021 | no code linked | report | |
| 30 | Frustum VoxNet (+3D ResNetFCN6) | 37.7 | 0.16 | – | Paper | – | 2019 | no code linked | report | |
| 31 | Frustum VoxNet (YOLO v3+3D ResNetFCN6) | 0.048 | – | Paper | – | 2019 | no code linked | report | ||
| 32 | DSS | 19.55 | – | Paper | – | 2015 | no code linked | report |
All 32 rows shown. 32 link to a paper page on this site; 1 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 11 rows have a graph line, from 10 distinct papers; 11 rows (10 papers) have at least one sample that ran. Counting each paper once: Syntology ran 37 of 90 samples; 53 unverified. Separately, 14 of those 90 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.
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