# Orientation-boosted Voxel Nets for 3D Object Recognition

arXiv: https://arxiv.org/abs/1604.03351 · HTML: https://mlsanity.com/paper/1604.03351/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 17 of 17)

- [CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training](https://mlsanity.com/paper/2210.01055/) (2022)
- [SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network](https://mlsanity.com/paper/2104.07861/) (2021)
- [Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition](https://mlsanity.com/paper/2101.02374/) (2021)
- [MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step](https://mlsanity.com/paper/2411.01208/) (2024)
- [Steerable Transformers for Volumetric Data](https://mlsanity.com/paper/2405.15932/) (2024)
- [Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views](https://mlsanity.com/paper/2108.03743/) (2021)
- [Image to Icosahedral Projection for SO(3) Object Reasoning from Single-View Images](https://mlsanity.com/paper/2207.08925/) (2022)
- [VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstruction](https://mlsanity.com/paper/2203.07553/) (2022)
- [3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review](https://mlsanity.com/paper/2306.05978/) (2023)
- [Category-Agnostic Pose Estimation for Point Clouds](https://mlsanity.com/paper/2403.07437/) (2024)
- [Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2407.01810/) (2024)
- [TACO-Net: Topological Signatures Triumph in 3D Object Classification](https://mlsanity.com/paper/2509.24802/) (2025)
- [Spherical Transformer: Adapting Spherical Signal to CNNs](https://mlsanity.com/paper/2101.03848/) (2021)
- [A deep perceptual metric for 3D point clouds](https://mlsanity.com/paper/2102.12839/) (2021)
- [MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation](https://mlsanity.com/paper/2105.13277/) (2021)
- [What Stops Learning-based 3D Registration from Working in the Real World?](https://mlsanity.com/paper/2111.10399/) (2021)
- [Meta Episodic learning with Dynamic Task Sampling for CLIP-based Point Cloud Classification](https://mlsanity.com/paper/2404.00857/) (2024)

## Abstract (arXiv)

Recent work has shown good recognition results in 3D object recognition using 3D convolutional networks. In this paper, we show that the object orientation plays an important role in 3D recognition. More specifically, we argue that objects induce different features in the network under rotation. Thus, we approach the category-level classification task as a multi-task problem, in which the network is trained to predict the pose of the object in addition to the class label as a parallel task. We show that this yields significant improvements in the classification results. We test our suggested architecture on several datasets representing various 3D data sources: LiDAR data, CAD models, and RGB-D images. We report state-of-the-art results on classification as well as significant improvements in precision and speed over the baseline on 3D detection.