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Feedback-Driven Learning for 3D Object Detection and Completion with Limited Supervision
by
Ananthu Ajith
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 94 |
| Year of Publication: 2026 |
| Authors: Ananthu Ajith |
10.5120/ijca2026926636
|
Ananthu Ajith . Feedback-Driven Learning for 3D Object Detection and Completion with Limited Supervision. International Journal of Computer Applications. 187, 94 ( Mar 2026), 61-68. DOI=10.5120/ijca2026926636
@article{
10.5120/ijca2026926636,
author = {
Ananthu Ajith
},
title = { Feedback-Driven Learning for 3D Object Detection and Completion with Limited Supervision },
journal = {
International Journal of Computer Applications
},
issue_date = { Mar 2026 },
volume = { 187 },
number = { 94 },
month = { Mar },
year = { 2026 },
issn = { 0975-8887 },
pages = {
61-68
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume187/number94/feedback-driven-learning-for-3d-object-detection-and-completion-with-limited-supervision/
},
doi = { 10.5120/ijca2026926636 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-03-29T02:17:20.519721+05:30
%A Ananthu Ajith
%T Feedback-Driven Learning for 3D Object Detection and Completion with Limited Supervision
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 94
%P 61-68
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Object detection and completion in 3D have shown considerable potential in the field of autonomous vehicles, robots, and mixed reality, but it heavily depends on dense 3D annotations, which is difficult to scale. In this work, the authors have proposed a feedback-driven learning framework for 3D perception with lim-ited supervision. It generates pseudo-labels using weak supervision and refines them using a closed-loop process, which includes the use of vision foundation models along with geometry-aware con-straints. This has shown considerable potential in the field of 3D object detection and completion in a scalable and robust manner.
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