project_vidar

VIDAR — LCF3D Late-Cascade Fusion for 3D Object Detection

Part of the ANIMA Perception Suite by Robot Flow Labs.

Paper

LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Driving Carlo Sgaravatti, Riccardo Pieroni, Matteo Corno, Sergio M. Savaresi, Luca Magri, Giacomo Boracchi (2026)

Paper on HuggingFace | GitHub

Architecture

VIDAR implements the LCF3D pipeline — a two-stage fusion approach that combines:

1. Late Fusion: Match LiDAR 3D detections with RGB 2D detections to filter false positives
2. Cascade Fusion: Generate 3D frustum proposals from unmatched RGB detections to recover missed objects

The PointNet++ backbone processes raw 3D point clouds (16,384 points) and outputs 3D bounding boxes with class predictions for Car, Pedestrian, and Cyclist.

Model: PointNet++ (512,202 parameters) Input: Point cloud (B, N, 3) — xyz coordinates Output: 3D detections (B, K, 10) — [cx, cy, cz, dx, dy, dz, yaw, class, confidence, ...]

Exported Formats

Usage

PyTorch

import torch
from anima_vidar.detectors.pointnet_pp import PointNetPPDetector

model = PointNetPPDetector(num_classes=3, input_dim=3)
state = torch.load("pytorch/vidar_v1.pth")
model.load_state_dict(state)
model.eval()

# Inference
points = torch.randn(1, 16384, 3)  # (batch, num_points, xyz)
detections = model(points)  # (1, 128, 10)

SafeTensors

from safetensors.torch import load_file
state = load_file("pytorch/vidar_v1.safetensors")
model.load_state_dict(state)

ONNX

import onnxruntime as ort
session = ort.InferenceSession("onnx/vidar_v1.onnx")
result = session.run(None, {"point_cloud": points_numpy})

Training Results

Target Metrics (Paper Table 1, KITTI Moderate)

Sensor Stack

License

Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED

Training Report

See TRAINING_REPORT.md for detailed training curves and final metrics.

Model Info

• Parameters: 512,202
• Input shape: [1, 16384, 3]