Papers › Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D...
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
Zhixin Wang, Kui Jia
In this work, we propose a novel method termed \emph{Frustum ConvNet (F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum-level features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of F-ConvNet, including an FCN variant that extracts multi-resolution frustum features, and a refined use of F-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. F-ConvNet assumes no prior knowledge of the working 3D environment and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: {\url{https://github.com/zhixinwang/frustum-convnet}.}
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
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="1903.01864")
Code
Syntology Ran 1 of 16 code samples harvested from 1 repository linked to this paper; 15 have no recorded run. Of those that ran: 1 ran · fixture could not drive it.
By repository: official repository: 16 samples from 1 repository, 1 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
16 samples harvested; 1 ran; 0 honoured the contract we drafted; 15 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.
Licence: 0 of the 16 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 zhixinwang/frustum-convnet. “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.
aab935d4b7b55c2c · report
df9e498714c411f2 · report
ce58b42def83bfb4 · report
e0f1f2d3ccac5f2e · report
3b86dc1730e8d1c1 · report
f47e771020b20fef · report
0c5c08f1db6108da · report
8d0c2b6ba5dcbf2d · report
4b69faf22a81a1e4 · report
281f3748f309afc2 · report
f6eae958a22ae877 · report
a124059a5c42fe93 · report
fda77318c74af228 · report
9989d7e45fda73c4 · report
53a9950814898b9c · report
ce156beb68c9daa3 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | KITTI Cars Easy | F-ConvNet | AP | 85.88% | #17 of 26 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Hard | F-ConvNet | AP | 68.08% | #17 of 25 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cyclists Easy | F-ConvNet | AP | 79.58% | #3 of 12 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cyclists Hard | F-ConvNets | AP | 57.03% | #5 of 12 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cyclists Moderate | F-ConvNet | AP | 64.68% | #4 of 13 | Archive leaderboard | report |
| 3D Object Detection | KITTI Pedestrians Easy | F-ConvNet | AP | 52.37% | #4 of 9 | Archive leaderboard | report |
| 3D Object Detection | KITTI Pedestrians Hard | F-ConvNet | AP | 41.49% | #4 of 9 | Archive leaderboard | report |
| 3D Object Detection | KITTI Pedestrians Moderate | F-ConvNet | AP | 43.38% | #6 of 12 | 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