Research map / Point clouds / Segmentation and detection
Beyond Virtual Points: Depth-Enhanced LiDAR-only 3D Object Detection with Semi-Supervised Learning (Student Abstract)
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Replaces the depth-completion virtual-point step of VirConv with a cheap depth-enhancement augmentation (noise points added around the k farthest LiDAR points) plus pseudo-label semi-supervised learning, yielding LiDAR-only 3D detectors (BVPConv) that are faster on the KITTI leaderboard.
Problem
LiDAR-only 3D object detection suffers from sparse points at long range, and the strongest multimodal fix (virtual points from depth completion, as in VirConv) is too slow for real-time use.
Approach
Drop the depth completion stage entirely and instead densify distant LiDAR points during training only via a percentile-based depth enhancement (adding n random noise points around each of the k farthest points), combined with a teacher-student semi-supervised scheme using pseudo-labels from a LiDAR+virtual-point teacher.
Key results
- BVPConv-L runs at 0.01 s per sample, the fastest in the leaderboard table, while keeping a Car 3D AP sum of 256.80-range scores near the top (BVPConv-L 3D sum 255.85 vs VirConv-T 260.03 at 0.09 s).
- BVPConv-T achieves Car 3D AP sum 256.80 at 0.05 s, versus 260.03 for VirConv-T at 0.09 s, trading a small accuracy drop for roughly half the latency.
- Depth enhancement removes the depth completion stage, reported as more than a twofold speed increase over the virtual-point pipeline.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace depth completion with depth enhancement and semi-supervised learning | VirConv-L · KITTI leaderboard test | Inference time: 0.09 -> 0.01 | Helped |
| Replace depth completion with depth enhancement and semi-supervised learning | VirConv-L · KITTI leaderboard test | Car 3D AP Sum: 262.13 -> 255.85 | Hurt |
| Replace depth completion with depth enhancement and semi-supervised learning | VirConv-T · KITTI leaderboard test | Inference time: 0.09 -> 0.05 | Helped |
| Replace depth completion with depth enhancement and semi-supervised learning | VirConv-T · KITTI leaderboard test | Car 3D AP Sum: 260.03 -> 256.80 | Hurt |
Surprising findings
- The paper's claimed speed-accuracy balance rests on a leaderboard table where BVPConv-L's 3D AP sum (255.85) is below VirConv-S (262.13) and VirConv-T (260.03), so the accuracy cost is nontrivial.
Limitations
- Accuracy is still below the virtual-point baselines (e.g., VirConv-S, VirConv-T) on the 3D AP sums.
- Depth enhancement hyperparameters (n, sigma, k) were fixed at single values without reported sensitivity analysis.
- Evaluation is limited to KITTI leaderboard results with no ablation tables in the abstract-length paper.
Experimental setup
Datasets: KITTI 3D object detection KITTI odometry
Benchmarks: KITTI 3D object detection leaderboard
Metrics: BEV AP R40 3D AP R40 inference time (sec)
Baselines: VirConv-S VirConv-T TED LoGoNet SFD CasA++ CLOCs SE-SSD BVPConv-L BVPConv-T
Builds on: VirConv
Closest papers by meaning
- Semi-supervised 3D Object Detection with PatchTeacher and PillarMix (AAAI 2024 · similarity 0.88)
- GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection (CVPR 2025 · similarity 0.88)
- A-Teacher: Asymmetric Network for 3D Semi-Supervised Object Detection (CVPR 2024 · similarity 0.88)
- Pre-training LiDAR-based 3D Object Detectors through Colorization (ICLR 2024 · similarity 0.87)
- STONE: A Submodular Optimization Framework for Active 3D Object Detection (NeurIPS 2024 · similarity 0.87)
- ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network (CVPR 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.