Long3D Benchmark for 3D Reasoning

• The Long3D Benchmark defines dual evaluation tracks for both streaming 3D reconstruction and multi-modal 3D reasoning across extensive, unbroken data sequences.
• It rigorously quantifies global reconstruction quality using metrics like Accuracy, Chamfer Distance, and Normal Consistency over sequences up to 9545 frames.
• The benchmark supports ten tasks—from object detection to navigation planning—paving the way for scalable and semantically rich 3D AI research.

The Long3D Benchmark defines an advanced standard for evaluation in persistent 3D geometry understanding as well as multi-modal 3D reasoning with LLMs. Recent developments have introduced two distinct Long3D paradigms: one for streaming, long-horizon geometric reconstruction (Yuan et al., 5 Jan 2026), and another for multi-task 3D vision-LLM evaluation, also denoted as "3DBench" (Zhang et al., 2024). Collectively, Long3D enables rigorous testing of systems on unbroken data streams and richly structured 3D reasoning tasks, addressing the dual challenges of scalability and semantic diversity in contemporary 3D computer vision and artificial intelligence.

1. Benchmark Motivation and Scope

Long3D was conceived to address critical limitations in prior evaluation protocols for large-scale, continuous, and semantically rich 3D scene understanding. Streaming benchmarks were previously constrained by short sequence lengths and an inability to rigorously track drift and resource-bounded inference over thousands of frames. Similarly, multi-modal 3D benchmarks for vision-LLMs suffered from a narrow focus on classification and simple captioning tasks, lacking the breadth to probe spatial reasoning and planning. Long3D fills these gaps via two core tracks:

• Streaming 3D reconstruction: Unbroken video/LiDAR/IMU sequences up to 9545 frames for persistent global geometry evaluation (Yuan et al., 5 Jan 2026).
• Multi-task 3D perception and reasoning: 231,000 point cloud–instruction–answer pairs spanning ten task categories, including object detection, reasoning, expression, and planning (Zhang et al., 2024).

2. Dataset Composition and Acquisition

Streaming Long3D (InfiniteVGGT):

• Five continuous video sequences, each captured by a handheld 3D scanner (LiDAR + IMU + synchronized RGB), representing diverse indoor and large-scale outdoor scenes.
• Sequence lengths: Classroom (2128), Dormitory (4208), Library (4726), Badminton Court (6067), Academic Building (9545), mean ≈ 5335 frames.
• Sensor modalities encompass 800×600 RGB at 10 Hz, 3D LiDAR sweeps over a 360° × 59° FOV, and inertial pose priors.
• Rigid extrinsic calibration ensures cross-sensor alignment; ground-truth is produced via LiDAR+IMU fusion registered in a global frame.

3DBench (Multi-Modal Long3D):

• Synthesized using ProcTHOR and AI2-THOR for depth, RGB, and instance mask generation over 30,000 scenes.
• Instruction–answer pairs are generated with LLM prompting, filtered for quality (using perplexity and length), and annotated with rich metadata (object classes, 3D bounding boxes, room segmentation, connectivity graphs).
• Spans 93 object categories, scenes with 5–20 objects and 1–5 rooms, sampling uniformly across spatial complexity.

3. Principal Tasks and Supported Modalities

Streaming Long3D:

• Dense 3D reconstruction: end-to-end fusion of RGB into a persistent point cloud covering the full camera trajectory.
• Model evaluation is strictly online—frames processed sequentially, no resets or down-sampling, bounded GPU resources (validated on NVIDIA A100).
• By extension, per-frame depth, pose, and drift statistics can be extracted but are secondary.

Multi-Modal Long3D (3DBench):

• Ten core tasks including object classification, visual grounding, detection, counting, spatial relationship reasoning, room segmentation, object relations, captioning, question answering, and navigation planning.
• Input modalities vary per task: object- or scene-level point cloud, camera pose, natural-language instruction, and navigation coordinates.
• Output modalities include class labels, bounding boxes, spatial paths, yes/no descriptors, paragraphs, and multi-turn dialogues.

4. Evaluation Metrics and Protocols

Streaming Long3D Metrics

Predicted ($P_\text{pred}$) and ground-truth ($P_\text{gt}$) point clouds are rigidly aligned with ICP before metric computation:

• Accuracy (Acc):

$$\text{Acc}(P_\text{pred}, P_\text{gt}) = \frac{1}{|P_\text{pred}|} \sum_{x \in P_\text{pred}} \min_{y \in P_\text{gt}} \|x - y\|_2$$

• Completion (Comp):

$$\text{Comp}(P_\text{pred}, P_\text{gt}) = \frac{1}{|P_\text{gt}|} \sum_{y \in P_\text{gt}} \min_{x \in P_\text{pred}} \|x - y\|_2$$

• Chamfer Distance (CD):

$$\text{CD}(P_\text{pred}, P_\text{gt}) = \text{Acc} + \text{Comp}$$

• Normal Consistency (NC):

$$\text{NC}(P_\text{pred}, P_\text{gt}) = \frac{1}{|P_\text{pred}|} \sum_{x \in P_\text{pred}} |\langle n_x, n_{y^*} \rangle|$$

where $y^* = \arg\min_{y \in P_\text{gt}} \|x - y\|_2$.

No per-frame ATE or RPE is computed; focus is on global reconstruction quality.

3DBench (Multi-task Long3D) Metrics

• Discrete accuracy $(\mathrm{Acc})$: For classification, counting (hard assignments).

$$\mathrm{Acc} = \frac{1}{N}\sum_{i=1}^N \mathbf{1}[\,\hat y_i=y_i\,]$$

• Detection & Visual Grounding:

“In-box” if predicted center is within ground-truth bounding box; “around-box” if within 1 m of GT center.

• Navigation:

Path loss between predicted and ground-truth waypoint polylines, successful if $\mathrm{PathLoss} \leq \delta$ (e.g., $\delta=0.5$ m).

• Relation Reasoning:

F1 score for small fixed relation sets.

• Language Generation:

BLEU, CIDEr, METEOR for automatic scoring; LLM-based human-like evaluation via GPT-4 prompt (“rate from 1–5 on fluency, correctness, completeness”).

5. Experimental Protocols and Baseline Results

Streaming Long3D:

• “Test-only” evaluation: no train/val splits, zero-shot performance required.
• Methods must process all frames in timestamp order on a single GPU.
• Drift is tracked over the full sequence via error metrics (CD, NC); performance is optionally plotted on frame prefixes to visualize temporal instability.

InfiniteVGGT achieves lowest Acc and CD across all sequences, with highest NC in most cases. On the 9545-frame Academic Building sequence, InfiniteVGGT reduces CD from ≈6.95 m (TTT3R) to 3.47 m (Yuan et al., 5 Jan 2026). Offline batch methods (VGGT, streamVGGT) exceed memory budgets and cannot complete full sequences.

Multi-modal Long3D (3DBench):

• Three representative 3D-LLMs (LAMM-7B, PointLLM-7B, Point-LLM-7B) evaluated under zero-shot and fine-tuned regimes.
• Zero-shot accuracy ranges:
  • Classification ($\leq$16%)
  • Visual Grounding (“in-box” $\leq$5%)
  • Detection (<1%)
  • Counting ($\leq$23%)
  • Navigation (<9%)
• Fine-tuning on Long3D yields absolute improvements:
  • Classification +19.1 pp
  • Counting +23.3 pp
  • Caption(object) +18.8 pp (CIDEr)
  • Navigation +18.2 pp
• Some QA/caption metrics drop slightly in LLM-based scoring, attributed to LLM mismatch.

6. Limitations, Insights, and Future Directions

Long3D reveals several limitations in current methodology:

• Vanilla point-cloud encoders saturate after 20k samples; performance plateaus for complex spatial reasoning tasks.
• Multi-object relation reasoning and navigation tasks persist below 30% accuracy post fine-tuning.
• Monotonous prompt templates induce repetitive model outputs.

Proposed future directions include stronger 3D backbones (sparse transformers, graph networks), multi-task co-training with cross-modal attention, progressive curriculum learning from easy to complex tasks, and benchmarks incorporating dynamic scenes.

3DBench prescribes best practices for robust evaluation: inclusion of object- and scene-scale tasks, broad competency axes, automated pipeline for reproducibility, diverse metric families, and semantic/spatial diversity. Suggestions for extension include physics/interaction challenges, noisy real-scans, multilingual and interactive dialogues.

7. Implementation and Usage Guidance

To benchmark a new method:

• Download the 5 point-cloud + RGB sequences from the InfiniteVGGT repository (Yuan et al., 5 Jan 2026).
• Run the streaming reconstruction method end-to-end on each sequence at 10 Hz, maintaining bounded resources.
• Align output to GT using ICP, then compute Acc, Comp, CD, NC as defined.
• Report mean and median metrics per scene; plot per-prefix errors to visualize drift.

For multi-modal model evaluation, employ 3DBench’s automated pipeline and protocol (Zhang et al., 2024), adhering to task-specific evaluation criteria and metric families.

A plausible implication is that Long3D establishes a foundational reference for persistent 3D perception and multi-modal reasoning, providing a clear roadmap for progress in robust, scalable, and semantically rich geometric AI.